{"as_of":"2026-08-08T04:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75de81bc1ef72aea1ea4fff4ca193486cc4776737db5ab2438b790e8f25eda11","coverage":[{"denominator":102,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:12:17.757071Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:07:37.473922Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T22:07:38.400522Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"cited_work":{"arxiv_id":"2505.22594","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.22594","snapshot_observed_at":"2026-08-06T22:07:38.400522Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","venue":"math.ST","work_id":"d0290548-1bf9-4366-b380-acaad015d35a","year":2025},"citing_paper":{"arxiv_id":"2506.23010","last_updated":"2025-06-28T20:48:56Z","snapshot_observed_at":"2026-08-07T08:44:55.289497Z","submitted_at":"2025-06-28T20:48:56Z","title":"On Universality of Non-Separable Approximate Message Passing Algorithms","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T22:07:37.473922Z"},"links":{"cited_paper":"/paper/2505.22594","citing_paper":"/paper/2506.23010"},"observation_digest":"sha256:485d807d6238bc54e90b178f73cf39950fed6cd3d33853d5621046654fe5182d","observation_id":"cdfc9c57-1ca9-4a5c-a06a-e1d2fba01bfb","resolution":{"observed_at":"2026-08-06T22:07:38.504313Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.22594/citation-record","integrity":"/paper/2505.22594/integrity","json":"/paper/2505.22594/citation-record.json","paper":"/paper/2505.22594"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.253823Z","title":"Predicting with proxies: Transfer learning in high dimension","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.253823Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ee632aafef9ef76903c1c3ec9bdc1a54a31e1a2718486f7911708ee0480c86a1","observation_id":"c469c302-03d7-46be-be7f-d9a1ec8d12b9","resolution":{"observed_at":"2026-08-07T13:12:09.253823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.332818Z","title":"Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.332818Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:210874716856275758fe3ebfb96dd6f882544aacbd96b80d48e03a7feea8aa78","observation_id":"5fdd57d3-1850-4493-96bd-60773733f623","resolution":{"observed_at":"2026-08-07T13:12:09.332818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.538972Z","title":"Near-optimal linear regression under distribution shift","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.538972Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:45a3ad2a7430769053b5b75ba04f95383df836a4097d538c92565c3e745b54dc","observation_id":"4a072955-ec92-4866-a8cc-3e2b131d7486","resolution":{"observed_at":"2026-08-07T13:12:09.538972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.617931Z","title":"Transfer learning for nonparametric classification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.617931Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6b00ae93e3c57e3964899c9a4eb6d34e02c8a4c1483b293911121ef259dba2c1","observation_id":"06f04121-8496-4c6b-bbea-53df3fb1c660","resolution":{"observed_at":"2026-08-07T13:12:09.617931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.755488Z","title":"A class of geometric structures in transfer learning: Minimax bounds and optimality","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.755488Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:7a42eb62f0331cb1537c485337c197dec6812393f092466bd70834a9e14d8a6e","observation_id":"099e2a1b-1e98-4e16-a24a-c3bc9266d273","resolution":{"observed_at":"2026-08-07T13:12:09.755488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.904324Z","title":"Searching for robust associations with a multi-environment knockoff filter","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:09.904324Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:4b8c4ae49c626e2f57787b0085c846602552ab3927e024a9bcd15760d93a0141","observation_id":"8058abf9-e104-4e69-90d3-8af813241098","resolution":{"observed_at":"2026-08-07T13:12:09.904324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.043447Z","title":"Individual data protected integrative regression analysis of high-dimensional heterogeneous data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.043447Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c381f7bae0102698c22a828d07b1a107c01987c3aa84f698f7a9ab6b71843600","observation_id":"d613a07e-7061-407b-b87e-59a7b1c51aeb","resolution":{"observed_at":"2026-08-07T13:12:10.043447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.208314Z","title":"Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.208314Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:e0a7ef3e8a6534bdfcbc5c3769bcfdebe26fcaa7d1e49364704a13f114e7c809","observation_id":"0d6a726e-f650-4f7a-8332-b4586e6b7666","resolution":{"observed_at":"2026-08-07T13:12:10.208314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.287461Z","title":"Adaptive and robust multi-task learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.287461Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:f6d916cbacf1576b4fc75a0f9ab767c58f1be8bd7114aa146701af2c067f2dc3","observation_id":"24895f5a-dfed-4462-97c5-dd84be0ab2bd","resolution":{"observed_at":"2026-08-07T13:12:10.287461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.399225Z","title":"Targeting underrepresented populations in precision medicine: A federated transfer learning approach","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.399225Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:53d30bf359094fda5aec602b875cfa7ab613eec7871ed8fd9a6467d9fe41c529","observation_id":"8e80db92-eec0-4e89-9830-23c3028a2410","resolution":{"observed_at":"2026-08-07T13:12:10.399225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12272","last_updated":"2024-01-22T16:24:04Z","snapshot_observed_at":"2026-07-06T17:19:00.228875Z","submitted_at":"2024-01-22T16:24:04Z","title":"Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12272","snapshot_observed_at":"2026-08-07T13:12:10.486483Z","title":"Transfer learning for nonparametric regression: Non-asymptotic minimax analysis and adaptive procedure","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.486483Z"},"links":{"cited_paper":"/paper/2401.12272","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:43fde9e4e2ec7dd88a99d9e06bc341e7c0e1c103b9833e3900dc1fd35ea44098","observation_id":"5694e879-b701-4bb8-9972-35e616f9b1d6","resolution":{"observed_at":"2026-08-07T13:12:10.486483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.628323Z","title":"Statistical challenges of high-dimensional data, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.628323Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a6f1422aebc053056c8d94e7b0b349acae0b679eceb3f06e9a323f7fe7419813","observation_id":"70d7b6fb-9a44-409a-b59d-b4c9103fc0a4","resolution":{"observed_at":"2026-08-07T13:12:10.628323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.770943Z","title":"Message-passing algorithms for compressed sensing","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.770943Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a990236fb2a4378669afd289b36f7781bec5ea406be978991f0a8426fa26e0de","observation_id":"4c9c865c-a10a-45a8-9768-f9bfbb3468ef","resolution":{"observed_at":"2026-08-07T13:12:10.770943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.904720Z","title":"Optimal m-estimation in high-dimensional regression","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:10.904720Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:49891bc98f1fe28532c29c66d9c01ad4a75ec68fda25dfdffc6818254001e4b9","observation_id":"4c21a413-8950-4d74-9ec4-9a220920b09f","resolution":{"observed_at":"2026-08-07T13:12:10.904720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.014413Z","title":"Precise error analysis of regularized m-estimators in high dimensions","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.014413Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:9f89c2d05666d18a8e6fc3d5ce29fe341da514a1dc7c247adf0aa8ec2a71f49a","observation_id":"30edaa44-28a2-41c2-abf4-0895449a938c","resolution":{"observed_at":"2026-08-07T13:12:11.014413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.148911Z","title":"The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.148911Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ba5d1ac5d503e47f4a7a570d1d212a195667afb4d534a8575015774af0ce2a7d","observation_id":"b0b71b65-1678-4a7a-b87e-f197bb0e54fa","resolution":{"observed_at":"2026-08-07T13:12:11.148911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.250621Z","title":"A modern maximum-likelihood theory for high-dimensional logistic regression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.250621Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:3788d2476599afb1630c08ec73342bcee557d0c4f8284e209a2853b1a31f0411","observation_id":"3670d51d-c80d-4dce-97a8-d0ac62fa1ef6","resolution":{"observed_at":"2026-08-07T13:12:11.250621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.315027Z","title":"The impact of regularization on high-dimensional logistic regression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.315027Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:4fdddb23eeb61db8454841af91c6662adcbacf20c9455e33bc59755ee7a46807","observation_id":"f64e1d64-6c0c-4f90-ad68-4ed060e12540","resolution":{"observed_at":"2026-08-07T13:12:11.315027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.384908Z","title":"The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.384908Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:e021c1c0fa806b9a37d4a9bc7d385f0d4e2622b9a32df96cddbf9874e00bc332","observation_id":"e78fa727-3c9c-4aeb-b0e8-f830f34e4e81","resolution":{"observed_at":"2026-08-07T13:12:11.384908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.505635Z","title":"Optimal errors and phase transitions in high-dimensional generalized linear models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.505635Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:7229c1288c15dbb6315df97d7a1815e2409a3b6806b7ae6cce350cfe8f3cf4f1","observation_id":"06567519-96f6-4586-b052-85fa8b6e6e05","resolution":{"observed_at":"2026-08-07T13:12:11.505635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/19-aos1906","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:18.096535Z","title":"Which bridge estimator is the best for variable selection? The Annals of Statistics , 48(5):2791 – 2823, 2020","venue":null,"work_id":"16e1f70b-5cfd-4184-94e6-45e699c6d19e","year":2020},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.609894Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:9a42bb18bdcee95995016c1a015e62180da4a3f8eace2cc79e92e9bc41c62186","observation_id":"e532b1d9-b159-4691-b522-9a20ada290fd","resolution":{"observed_at":"2026-08-07T13:12:18.155988Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.724862Z","title":"Approximate message passing with spectral initialization for generalized linear models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.724862Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:be86baf3b07e457fd487398c8f1349e2f89f0903ec03b01fc8c4b279f4aecc3b","observation_id":"10530f05-5128-456d-9638-f6e6dfcb9e4a","resolution":{"observed_at":"2026-08-07T13:12:11.724862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.841138Z","title":"Phase transitions in transfer learning for high-dimensional perceptrons","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.841138Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:20abfc2a785312a8429af094aff35820a5a6be99302c8cab57ca0cfe51fdb910","observation_id":"b89d369b-5959-4901-82eb-60b54a58b1b0","resolution":{"observed_at":"2026-08-07T13:12:11.841138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.989717Z","title":"The asymptotic distribution of the mle in high-dimensional logistic models: Arbitrary covariance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:11.989717Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:78cc11c2ec2224dedbdc77adc5a9b7e9e41f02a01dc0b6bd61ff99be06be1888","observation_id":"4453c70a-a4ac-4c8b-b193-88fe0924f29d","resolution":{"observed_at":"2026-08-07T13:12:11.989717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10198","last_updated":"2022-10-28T20:50:35Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T14:17:53Z","title":"A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond","version":3},"cited_work":{"arxiv_id":"2205.10198","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.10198","snapshot_observed_at":"2026-08-07T13:12:19.722174Z","title":"A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond","venue":"math.ST","work_id":"c03b66af-db00-48bd-a5ee-608474d887c4","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.150888Z"},"links":{"cited_paper":"/paper/2205.10198","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ab1e4335007c8b2c0b6ddceba0fc4abc123647e203cf955c3013d267660eb7ac","observation_id":"ffb5a7e5-ad1d-43b9-bd02-c911798fde62","resolution":{"observed_at":"2026-08-07T13:12:19.815506Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:32.638734Z","title":"Surprises in high-dimensional ridgeless least squares interpolation","venue":null,"work_id":"e7375d5a-1799-4f97-8ea1-188381125d76","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.250774Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:f53a81fc3bce74806ec0affa9285eda5e41cfb1ac6885b8c62f1162e4b395bb6","observation_id":"4fc25aab-d7af-411c-b3ca-840212129ebf","resolution":{"observed_at":"2026-08-07T13:12:32.736720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:32.486541Z","title":"A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers","venue":null,"work_id":"9d2baf45-4c73-4591-bc0f-f0d20e4522af","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.306326Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:b960fc5480b925dcbd445a667881ef9adca5fa641e2f7e7c4c9869fa24ac4ea8","observation_id":"79b217b1-eee1-4cfe-be41-8a19348d3825","resolution":{"observed_at":"2026-08-07T13:12:32.567584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:32.344607Z","title":"The lasso with general gaussian designs with applications to hypothesis testing","venue":null,"work_id":"f5812638-0538-45ef-a429-7a75b514b57d","year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.394079Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:d1f908ebe463db0c91bc2dcbe3d1df2edccc69245aecd750fa2063662b80dfeb","observation_id":"9b7b01f7-16f7-4279-aec5-f17d77b26abb","resolution":{"observed_at":"2026-08-07T13:12:32.413736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11184","last_updated":"2024-06-17T03:47:56Z","snapshot_observed_at":"2026-07-06T18:31:53.591578Z","submitted_at":"2024-06-17T03:47:56Z","title":"HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11184","snapshot_observed_at":"2026-08-07T13:12:12.474772Z","title":"Hede: Heritability estimation in high dimensions by ensembling debiased estimators","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.474772Z"},"links":{"cited_paper":"/paper/2406.11184","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:5110620b4b809045561fc8a1a09058d73f035f724400512a4a8af0000be255bc","observation_id":"9007dfdd-8a57-4b51-b6e4-b0582a4690ac","resolution":{"observed_at":"2026-08-07T13:12:12.474772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2406.11666","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:19.492857Z","title":"Roti-gcv: Generalized cross-validation for right-rotationally invariant data","venue":null,"work_id":"f6242f41-e482-45cf-a5ef-1ffe83580e03","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.566635Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:22f08f4cd734b36c450836696b1eddd43afeec815401c760b8649de5fbc9ef22","observation_id":"f3179fdd-e7af-4533-a0a4-0fa85034f92a","resolution":{"observed_at":"2026-08-07T13:12:19.573208Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15131","last_updated":"2026-07-14T06:06:32Z","snapshot_observed_at":"2026-08-07T18:00:13.571950Z","submitted_at":"2025-02-21T01:24:27Z","title":"Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling","version":5},"cited_work":{"arxiv_id":"2502.15131","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.15131","snapshot_observed_at":"2026-08-07T13:12:19.217966Z","title":"Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling","venue":"math.ST","work_id":"f4e3ac7f-68d7-4a26-9d20-a4397b037340","year":2025},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.649628Z"},"links":{"cited_paper":"/paper/2502.15131","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:587206c8d5bfca256c776ccb872bd615ba1a861ccf571ae8efec2cd1d1763683","observation_id":"6b810f46-19af-4423-975e-8eb00e159e28","resolution":{"observed_at":"2026-08-07T13:12:19.296550Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:32.180142Z","title":"The lasso risk for gaussian matrices","venue":null,"work_id":"99f0d2ed-56ed-4da1-aa91-1c8bca3ad396","year":1997},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.726705Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:b12d0c57cc51257deeee46b2bbd85cd2f4ba8898b6a8192f96e4c01e4c09c76d","observation_id":"33158c93-f62f-4c5c-a21b-f7aa98c43f9d","resolution":{"observed_at":"2026-08-07T13:12:32.257048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:32.013721Z","title":"High dimensional robust m-estimation: Asymptotic variance via approximate message passing","venue":null,"work_id":"7fba3a08-373c-4605-8ad9-be33cf282555","year":2016},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.800126Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:2a910e1815ad5f82c8297521699bb9596df046042c5859e3d05b076af9847a74","observation_id":"95ca507f-6981-47a8-851a-8e1ab336f60a","resolution":{"observed_at":"2026-08-07T13:12:32.092999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:31.814415Z","title":"Statistical physics of inference: Thresholds and algorithms","venue":null,"work_id":"cac459bc-af40-4769-9f69-6818fba169bc","year":2016},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.886911Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:7fa1d39a5cd887b44388636d675e613e260be7c2ca1a44b4fd3a446f7c844a54","observation_id":"dc78e5ea-eadb-4e67-8f6e-46d92d840d1d","resolution":{"observed_at":"2026-08-07T13:12:31.912364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:31.642147Z","title":"A unifying tutorial on approximate message passing","venue":null,"work_id":"8f10b390-e16a-4378-a2ea-cb9cf00cd392","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:12.955973Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:112e6429e8be91ffa8f4995a4fb76a3e87920cbd2368f9b4145ca3c0f6b6d18f","observation_id":"11333a43-0d1f-4ff2-ab87-ada504f0ee05","resolution":{"observed_at":"2026-08-07T13:12:31.722616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:31.462120Z","title":"A friendly tutorial on mean-field spin glass techniques for non-physicists","venue":null,"work_id":"16e3c965-6dcc-44d9-b438-13e58a0ed47c","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.032624Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:24054272365f98ff36234087419f2482a3e5839a5d328eb1529e40f122ff24f5","observation_id":"709e5e3a-87dd-4763-809e-fe503defecf8","resolution":{"observed_at":"2026-08-07T13:12:31.545943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:31.297454Z","title":"An iterative construction of solutions of the tap equations for the sherrington–kirkpatrick model","venue":null,"work_id":"b1119d82-ee86-4a49-aeb9-2889f5de250c","year":2014},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.140687Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:9e5121de3689a2fe49854119a34aa944a3a7458f9245bee4aa575e2862587449","observation_id":"2e61a636-c585-4df4-824f-81a5d3a8170a","resolution":{"observed_at":"2026-08-07T13:12:31.384571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:31.065895Z","title":"The dynamics of message passing on dense graphs, with applications to compressed sensing","venue":null,"work_id":"65763ed4-ad38-4026-8c64-ad4dea0c5c77","year":2011},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.191986Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:0f869a89acd6dd5a2493f3d9fe61fb2fafe1516cc68abd80de0aa39cca65f53a","observation_id":"19adda42-525d-420c-be57-1e1966540eb8","resolution":{"observed_at":"2026-08-07T13:12:31.170013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:30.861520Z","title":"Generalized approximate message passing for estimation with random linear mixing","venue":null,"work_id":"7ec6f2fc-5613-41b2-b697-6e30a96a70a9","year":2011},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.293907Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:cf778159d3affdce7c7d59508bfbadf9626221786960ff6b55ca787884ff47ab","observation_id":"3d979803-b723-44d7-983f-da0d5c0fcfb0","resolution":{"observed_at":"2026-08-07T13:12:30.987066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:30.631845Z","title":"State evolution for general approximate message passing algorithms, with applications to spatial coupling","venue":null,"work_id":"13f7636c-80b0-4f2d-8271-d146598beb92","year":2013},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.378451Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:4387efc30d2643e4733379dbfd2a8143567a5205ecaf5857d96ce1533cc16031","observation_id":"8e1bf99c-bd51-4375-8c5d-908e8051573e","resolution":{"observed_at":"2026-08-07T13:12:30.704874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:30.445034Z","title":"State evolution for approximate message passing with non-separable functions","venue":null,"work_id":"cb3234cc-6219-421f-bc54-7fdcefc38ba4","year":2020},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.502096Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:787656c1dd1cc3a1e1dd42afc7c59ff95a49942c6de78da750f7821349b655fb","observation_id":"b926f5d1-8f8a-40ea-874e-85dd65f957d2","resolution":{"observed_at":"2026-08-07T13:12:30.534310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:30.226053Z","title":"Graph-based approximate message passing iterations","venue":null,"work_id":"9f740ea3-20bf-43e1-9127-1ccaaff11407","year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.595921Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c88cf7adf7d566e6649315a3bba62465201cd430d461f153d0c32c7e2fe82a46","observation_id":"02fba40d-f697-48ed-96d8-c112d55140d8","resolution":{"observed_at":"2026-08-07T13:12:30.315452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:30.079080Z","title":"Solution of’solvable model of a spin glass’","venue":null,"work_id":"6a686495-0980-4724-a55d-dbedb7022212","year":1977},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.660789Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:39f9a5849a59c0fae4851ba0592176d92e8baf43251096ba7a70bde03aced1c6","observation_id":"fa262f9c-4606-4353-b5f5-33c84aace974","resolution":{"observed_at":"2026-08-07T13:12:30.141101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:29.879808Z","title":"Graphical models concepts in compressed sensing","venue":null,"work_id":"62a6af4e-6592-4004-b511-07ffaf52fc33","year":2012},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.741300Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:dc31c3e398ec1a62cba0b0673f0283225e5913a1c8c5edfab37514e28ce53da8","observation_id":"a7a90669-1953-4bd4-b4cb-4770855b3d41","resolution":{"observed_at":"2026-08-07T13:12:30.001826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:29.758468Z","title":"Estimating lasso risk and noise level","venue":null,"work_id":"2d673752-abe2-4581-88a7-0e175b6a0f72","year":2013},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.802464Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:f18dc6c3c29b63cb31cdf639fa0872c0b92bae0e74df799cfe3d4a6f8e4b0cc1","observation_id":"2079a54e-e16d-4174-8a06-f97ea4b0303c","resolution":{"observed_at":"2026-08-07T13:12:29.816882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:29.527674Z","title":"Non-negative principal component analysis: Message passing algorithms and sharp asymptotics","venue":null,"work_id":"793d8767-dbe6-477c-bdcf-b3841562cc1a","year":2015},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:13.939230Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:da9a5917192630b0d3a732928f34ff0a06772b81b6f97047cd5d17346cd3034b","observation_id":"cb40b077-5982-48b8-b345-eb26a5c49280","resolution":{"observed_at":"2026-08-07T13:12:29.648035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:29.385063Z","title":"Asymptotics of map inference in deep networks","venue":null,"work_id":"2dde8b76-784c-44e9-b6e6-42c64dca47bf","year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.028597Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c10333e315c498f1bbf3fbfb50fbd1e12ee90539433ddd7ecf27b2a8c1a175de","observation_id":"b1e6b82b-3c70-4f49-9e11-237b29167da3","resolution":{"observed_at":"2026-08-07T13:12:29.457241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:29.107750Z","title":"Approximate message-passing decoder and capacity achieving sparse superposition codes","venue":null,"work_id":"d054d8a9-541b-483f-bb75-6999e8ffd215","year":2017},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.101685Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:585be15fc99621b4ff9c6fc9c6d479968fd98507e01f15d6941db3028c01a210","observation_id":"878c5ebc-4f7a-4d4f-88fa-c6551c97c881","resolution":{"observed_at":"2026-08-07T13:12:29.255421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:28.898331Z","title":"Vector approximate message passing","venue":null,"work_id":"ba6a8445-7df0-44a8-b53b-97280c422e63","year":2019},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.175154Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a01c5a3f8efac25d307675690d53e7f9836e1b3e8b66ab144363f12d8ff11f8d","observation_id":"805bda2b-24a5-4929-8508-24e391589bd6","resolution":{"observed_at":"2026-08-07T13:12:29.002354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:28.648272Z","title":"Orthogonal amp","venue":null,"work_id":"d7591f56-99ad-4ff2-9a12-a0624f63cb27","year":2020},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.211053Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:5221b0532381b0f60d17e46a4f9a79b98d19bbe87f806d12d9d3d0bd77accca8","observation_id":"3a0042e1-d778-4106-af15-3be0335f46a4","resolution":{"observed_at":"2026-08-07T13:12:28.772781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:28.448027Z","title":"Approximate message passing algorithms for rotationally invariant matrices","venue":null,"work_id":"3822f09d-9dfe-4580-8092-e197750c0523","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.269134Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:17304a8186027e5f34c77667a8d0b621b26a19b69504bb24cddffde27781f2f9","observation_id":"100d5c33-1588-428c-a618-8dd165970164","resolution":{"observed_at":"2026-08-07T13:12:28.545205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:28.166407Z","title":"Finite sample analysis of approximate message passing algorithms","venue":null,"work_id":"6c102aea-4ee2-4b8a-8694-fca21ec6a6c9","year":2018},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.340117Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:badc57a9c3bc896eec67f572dc58206b4cbe43ea34972812adf759ce21df31e4","observation_id":"96a1a5e9-7388-4483-a37e-f79bfd259656","resolution":{"observed_at":"2026-08-07T13:12:28.324697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.03313","last_updated":"2023-03-17T01:06:57Z","snapshot_observed_at":"2026-07-06T13:39:17.572174Z","submitted_at":"2022-08-05T17:59:06Z","title":"A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.03313","snapshot_observed_at":"2026-08-07T13:12:14.416167Z","title":"A non-asymptotic framework for approximate message passing in spiked models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.416167Z"},"links":{"cited_paper":"/paper/2208.03313","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:e08d79ff401995555cf844d554bd2be65ef50dfb92f2ee3eff877dc4ef33cb66","observation_id":"1dc2251c-7e3d-401f-84c7-df86569ef0cc","resolution":{"observed_at":"2026-08-07T13:12:14.416167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:27.963002Z","title":"Transfusion: Covariate-shift robust transfer learning for high-dimensional regression","venue":null,"work_id":"bcc4e722-d677-4110-bda9-1a6bf03ac8ae","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.466654Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:e73c163003d636f15c125722fbece8a35ad32288f537bae2daedea4ecb666b40","observation_id":"8152b914-8254-4f0e-98d6-89cf33fe6799","resolution":{"observed_at":"2026-08-07T13:12:28.061552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07972","last_updated":"2024-01-03T09:01:24Z","snapshot_observed_at":"2026-07-06T16:47:12.709738Z","submitted_at":"2023-11-14T07:53:42Z","title":"Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07972","snapshot_observed_at":"2026-08-07T13:12:14.520230Z","title":"Residual importance weighted transfer learning for high-dimensional linear regression","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.520230Z"},"links":{"cited_paper":"/paper/2311.07972","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a714eefab68c8465a46ea63d47fb988258ae09c22efaff67f12c00ca3b611152","observation_id":"b0e0a5b3-4f4c-447a-8292-87ca9955d461","resolution":{"observed_at":"2026-08-07T13:12:14.520230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:27.726037Z","title":"Algorithmic analysis and statistical estimation of slope via approximate message passing","venue":null,"work_id":"c6410334-8d1f-4dfe-bdb0-9abe809f15f5","year":2020},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.601949Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a970140429d92882896f6b3f172f25f29901fea596993bbaba556b2dffbdd779","observation_id":"6dd9effc-8d03-422d-a9f2-96e42a000513","resolution":{"observed_at":"2026-08-07T13:12:27.867825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01266","last_updated":"2022-02-22T02:20:48Z","snapshot_observed_at":"2026-07-06T11:25:32.650351Z","submitted_at":"2021-07-02T20:48:54Z","title":"Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01266","snapshot_observed_at":"2026-08-07T13:12:14.678333Z","title":"Asymptotic statistical analysis of sparse group lasso via approximate message passing algorithm","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.678333Z"},"links":{"cited_paper":"/paper/2107.01266","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:d29d26beb7d9c214ed0b5895c0f7ea7e8e5a1274cbc95a35c7c606ed39980d56","observation_id":"fc098763-3e68-462e-ae2e-ae39cc2c8ccc","resolution":{"observed_at":"2026-08-07T13:12:14.678333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07828","last_updated":"2021-07-16T11:19:49Z","snapshot_observed_at":"2026-07-06T11:29:44.222544Z","submitted_at":"2021-07-16T11:19:49Z","title":"Chi-square and normal inference in high-dimensional multi-task regression","version":1},"cited_work":{"arxiv_id":"2107.07828","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.07828","snapshot_observed_at":"2026-08-07T13:12:18.954876Z","title":"Chi-square and normal inference in high-dimensional multi-task regression","venue":"math.ST","work_id":"f6edfb85-fa5a-4071-810f-b640e8772906","year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.770711Z"},"links":{"cited_paper":"/paper/2107.07828","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c2d3b90cec8d91eed3acae87cf506f1d1d9f23043fb701f237646d4aa38302fe","observation_id":"a7b04d2e-bb88-4231-8971-e29fe7227efd","resolution":{"observed_at":"2026-08-07T13:12:19.047640Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11750","last_updated":"2025-06-09T23:26:52Z","snapshot_observed_at":"2026-07-06T10:07:15.228239Z","submitted_at":"2020-10-22T14:14:20Z","title":"Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11750","snapshot_observed_at":"2026-08-07T13:12:14.853849Z","title":"Analysis of information transfer from heterogeneous sources via precise high-dimensional asymptotics","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.853849Z"},"links":{"cited_paper":"/paper/2010.11750","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6dfabeb5b627a389afb707affc83676ed61943321e1f986c14561895c03d2f1b","observation_id":"94f00e75-1c2d-4df8-b647-0db267f344ae","resolution":{"observed_at":"2026-08-07T13:12:14.853849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08234","last_updated":"2021-11-16T05:23:28Z","snapshot_observed_at":"2026-08-03T17:51:35.120731Z","submitted_at":"2021-11-16T05:23:28Z","title":"Covariate Shift in High-Dimensional Random Feature Regression","version":1},"cited_work":{"arxiv_id":"2111.08234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.08234","snapshot_observed_at":"2026-08-07T13:12:18.790583Z","title":"Covariate Shift in High-Dimensional Random Feature Regression","venue":"stat.ML","work_id":"7994fb02-2f3c-4fd8-84e4-f2ab79c417af","year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:14.915925Z"},"links":{"cited_paper":"/paper/2111.08234","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:27fc6bb9fa95366681ea3257d3762ec7c64044b0669f049430affd3a746c461c","observation_id":"e266834e-4352-4d5f-920c-6e940ee66386","resolution":{"observed_at":"2026-08-07T13:12:18.856399Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13944","last_updated":"2026-06-27T02:08:29Z","snapshot_observed_at":"2026-08-06T20:36:35.792109Z","submitted_at":"2024-06-20T02:23:28Z","title":"Generalization error of min-norm interpolators in transfer learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13944","snapshot_observed_at":"2026-08-07T13:12:15.013115Z","title":"Generalization error of min-norm interpolators in transfer learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.013115Z"},"links":{"cited_paper":"/paper/2406.13944","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6cd4248603f3f93ce0ba3109b2a2a6a42fa8b5842d6364d6e91e02e6a9cc719e","observation_id":"d94fc74e-9845-4be3-9040-8b8fd4660086","resolution":{"observed_at":"2026-08-07T13:12:15.013115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01233","last_updated":"2024-04-01T16:51:19Z","snapshot_observed_at":"2026-07-06T17:54:03.923842Z","submitted_at":"2024-04-01T16:51:19Z","title":"Optimal Ridge Regularization for Out-of-Distribution Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01233","snapshot_observed_at":"2026-08-07T13:12:15.130606Z","title":"Optimal ridge regularization for out-of-distribution prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.130606Z"},"links":{"cited_paper":"/paper/2404.01233","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:9720a2a960ef5cb50b4c2ad9d8c0b18549c653613f02b9a889c6d2766525aa05","observation_id":"103bc2b9-18e7-44b9-9845-dc64c708ff5a","resolution":{"observed_at":"2026-08-07T13:12:15.130606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00522","last_updated":"2024-07-17T08:55:59Z","snapshot_observed_at":"2026-08-07T10:32:03.329376Z","submitted_at":"2024-03-31T01:41:57Z","title":"Minimum-Norm Interpolation Under Covariate Shift","version":2},"cited_work":{"arxiv_id":"2404.00522","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.00522","snapshot_observed_at":"2026-08-07T13:12:18.413925Z","title":"Minimum-Norm Interpolation Under Covariate Shift","venue":"cs.LG","work_id":"1579fe3e-5ae2-44dd-b24d-115992fee853","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.203994Z"},"links":{"cited_paper":"/paper/2404.00522","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:f47305e409a04257436096e9949d26455230c3b28a614c12aecbbad124630423","observation_id":"74109a72-65b3-46b2-9392-95571b25d365","resolution":{"observed_at":"2026-08-07T13:12:18.592010Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.16336","last_updated":"2024-11-13T14:13:58Z","snapshot_observed_at":"2026-07-06T17:49:52.751335Z","submitted_at":"2024-03-25T00:21:34Z","title":"Predictive Inference in Multi-environment Scenarios","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16336","snapshot_observed_at":"2026-08-07T13:12:15.273087Z","title":"Predictive inference in multi-environment scenarios","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.273087Z"},"links":{"cited_paper":"/paper/2403.16336","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6fe0fcaefc589d99fcbe4de313c9310466e5000a4ccd1429e7e6687d8fc312a1","observation_id":"0b136f8a-e059-4205-b499-48cfbb2c7e51","resolution":{"observed_at":"2026-08-07T13:12:15.273087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:27.466002Z","title":"The adaptive lasso and its oracle properties","venue":null,"work_id":"fa319286-72c2-4e4d-abf0-b8768863f28e","year":2006},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.322080Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6c9a726fd6de91adeb3a6511d6bcd6141eb1ecec15098e7ee8a4044a2ce8602a","observation_id":"3184af88-2bfb-4706-ae15-2213b0ea4fb7","resolution":{"observed_at":"2026-08-07T13:12:27.642599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:15.399770Z","title":"Universality in polytope phase transitions and message passing algorithms","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.399770Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:b6524f951e0ea739f6b10584e83fc9a9be146499389276e27bb94ff88b3b687d","observation_id":"57a6fa3f-52d3-405e-b225-d3311354739d","resolution":{"observed_at":"2026-08-07T13:12:15.399770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:27.194825Z","title":"Universality of approximate message passing algorithms","venue":null,"work_id":"7593b4df-d321-466e-b094-f42e8fcc4875","year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.451318Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:7fe6ab0bb0b9636e001a4549711caf92b49a7eeb618282b699ee5ce3ea31f7d7","observation_id":"8a113f0e-5a89-4e7b-9119-2ba59376f59c","resolution":{"observed_at":"2026-08-07T13:12:27.299286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:27.006188Z","title":"Universality laws for high-dimensional learning with random features","venue":null,"work_id":"07e2bf61-6af7-4717-8373-542c5a7aba58","year":1932},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.522725Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:61fd0074fa7d6001d2494b5e753fc5e679d6f02f4315d3ace5392cad7f9367ca","observation_id":"341814e3-fec2-47d7-9c16-4f09f6445dd9","resolution":{"observed_at":"2026-08-07T13:12:27.102994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:26.678264Z","title":"Universality of empirical risk minimization","venue":null,"work_id":"28ab8370-6b95-4887-8e01-e55fb5a022c7","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.594676Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:090571692f0c061c94e51c8dea08ecbc71ee386f816dd10f96c27a7f6c95d915","observation_id":"88c9dba1-e463-4394-9587-1a1b9950c323","resolution":{"observed_at":"2026-08-07T13:12:26.793554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:26.490243Z","title":"Lu, and Subhabrata Sen","venue":null,"work_id":"a0529527-4fe4-4ce3-82b3-b867bcfa1018","year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.658998Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:096baa894746fb508f722a44e89a9825394c09e8db096566093e35450ef3c6d4","observation_id":"d0493947-95e4-4774-8301-1728701ad5a2","resolution":{"observed_at":"2026-08-07T13:12:26.612385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:15.731928Z","title":"Spectrum-aware debiasing: A modern inference framework with applications to principal components regression","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.731928Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:8b934905474e5adb7ea8fd60e446b0b6992c93cead9e5dc30ebf2e0ca93ab85f","observation_id":"4d602991-b5b6-4b07-b9ca-ac7377d3796e","resolution":{"observed_at":"2026-08-07T13:12:15.731928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:26.272644Z","title":"Universality of approximate message passing algorithms and tensor networks","venue":null,"work_id":"48802d44-15ca-4c51-9b80-f4e497c614cd","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.830699Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:db07e36f6d5fa8f55ad04bd3f1565e3c5689275850725e70f2c2834d9e102f09","observation_id":"278f4c70-5e48-4dae-a1ad-8f197f72f247","resolution":{"observed_at":"2026-08-07T13:12:26.392822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:26.065533Z","title":"Universality in block dependent linear models with applications to nonlinear regression","venue":null,"work_id":"afa42e10-3cbb-4fa5-818b-867cb60397d4","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.932048Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:d05bc01d2c0d43e2559710a3334985fefa32e6cea35db0d22b6eb98d06f354cd","observation_id":"976cd99b-fa74-409d-9cc8-cf633a436803","resolution":{"observed_at":"2026-08-07T13:12:26.169173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:25.870583Z","title":"Universality in transfer learning for linear models","venue":null,"work_id":"e23f04f2-2e73-491f-b432-8c685c81f46d","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:15.997360Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:a8ae18fda58b9287e916bb0d5f025010d4bb50e83e232ec0ad4427de7cf85a50","observation_id":"36f9de21-2293-4819-abac-93cc1b00afcb","resolution":{"observed_at":"2026-08-07T13:12:25.960461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:25.627028Z","title":"Adaptive transfer learning","venue":null,"work_id":"b2bd3186-a41b-407d-a248-134530ff01f6","year":2021},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.068124Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ce89d3e8055c5bbd262b82b6cd59989db58605c0b132d51b356046c593722915","observation_id":"39688297-c07b-4652-be82-077e8b8c5d16","resolution":{"observed_at":"2026-08-07T13:12:25.756842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:25.365554Z","title":"A no-free-lunch theorem for multitask learning","venue":null,"work_id":"7511ecb3-d2c3-4c97-953f-e954536d6809","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.127025Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6f52922cd5e562649ecbac18c9a2e547dee6e992b5297b879ec72022d180b771","observation_id":"7ee35265-3feb-42af-a181-25eab4188158","resolution":{"observed_at":"2026-08-07T13:12:25.487254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:25.118684Z","title":"Estimation and inference for high-dimensional generalized linear models with knowledge transfer","venue":null,"work_id":"678236ad-8568-43f0-9740-ee7012d259c8","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.199974Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ce4d42caf348f6d8a682276956b33dc3017ef74b402675ecce537115a7ba84ca","observation_id":"c3ac8e2c-ce65-49f7-a072-be69cb6cabfd","resolution":{"observed_at":"2026-08-07T13:12:25.225048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:24.886521Z","title":"Transfer learning under high-dimensional generalized linear models","venue":null,"work_id":"78b0bbab-7be0-4d28-b8f0-29bcdefb4eee","year":2023},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.289312Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:492e27e38579a3282cb3686196845e85db2c8fe5f28778fb0a90bc053c4b7a89","observation_id":"9ae33f2c-14b6-4561-9869-e09c4fc28254","resolution":{"observed_at":"2026-08-07T13:12:25.019420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:24.674455Z","title":"A linear adjustment-based approach to posterior drift in transfer learning","venue":null,"work_id":"cf31ef80-ad73-4709-af1b-8984c9ff3612","year":2024},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.345109Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:4d9e25b6222f1bf9de5c02eaccec258c5e90df61df546eb84b85809cd8a74d40","observation_id":"1f634dc6-06b5-4258-898a-70cdb4f7e46e","resolution":{"observed_at":"2026-08-07T13:12:24.775696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:24.443851Z","title":"Inequalities for the trace of matrix product","venue":null,"work_id":"d8bc8423-fc3c-49db-bd98-fba1e858849e","year":1994},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.420803Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:d90022c493336b0948d07ae2531c1dee7b3137b3600ab0816849e2ba145d5dd8","observation_id":"6df784d6-828b-4eb9-a6fb-5d156baf0d99","resolution":{"observed_at":"2026-08-07T13:12:24.563009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:24.234885Z","title":"Lasso risk and phase transition under dependence","venue":null,"work_id":"96de768b-68d0-4a13-9abd-e8e2c42e8c9d","year":2022},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.499131Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:0b599a9bf1e367522e84d30159cf06f1d498936d0a2843f8c791cc89ef26faa9","observation_id":"95245660-e36d-49af-b567-790c8cf2b7b0","resolution":{"observed_at":"2026-08-07T13:12:24.321085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:24.024458Z","title":"Limit of the smallest eigenvalue of a large dimensional sample covariance matrix","venue":null,"work_id":"4f4e275c-d259-42b0-a911-fe1e5d6fe7c5","year":2008},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.545377Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:48dede1adfff6a23487b410ac6f555179897006c05df83c02a7eb4f68a2ac4f5","observation_id":"3795cbc9-a8e5-460f-9a83-0e4385264ae9","resolution":{"observed_at":"2026-08-07T13:12:24.130677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1011.3027","last_updated":"2011-11-23T21:11:38Z","snapshot_observed_at":"2026-08-04T12:57:10.755670Z","submitted_at":"2010-11-12T20:20:28Z","title":"Introduction to the non-asymptotic analysis of random matrices","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1011.3027","snapshot_observed_at":"2026-08-07T13:12:16.628724Z","title":"Matrix-valued symmetric AMP iterations with non-separable non-linearities","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.628724Z"},"links":{"cited_paper":"/paper/1011.3027","citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:6fa80935893a748d4984d7197c0c6ab5b4690148e530efb5d5ee665a29adf0c2","observation_id":"b89eb138-276c-4c88-ac71-d7211be005c0","resolution":{"observed_at":"2026-08-07T13:12:16.628724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:23.766349Z","title":"Lemma F.5) applies just like in the proof of Lemma 3.2, Bayati and Montanari [32]","venue":null,"work_id":"6db57545-888c-4242-9ced-49684dd3efa4","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.694080Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:f49313f8b0e8aff2383e0c30ed9caa9d9a4574972f4f4215732cb162d9ee30d4","observation_id":"bb4ff19f-ba46-4b2e-868e-cdd6532a538b","resolution":{"observed_at":"2026-08-07T13:12:23.905699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:23.560520Z","title":null,"venue":null,"work_id":"315ceebb-9f4f-45d9-8e08-4cc3ddbed3d1","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.751719Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c9c2609d341d3dd6d9a16aa39b0e81030037356fe1b76678e792b3fe4c057494","observation_id":"f073379b-f110-49ab-93d3-c3f2bfdcb7d8","resolution":{"observed_at":"2026-08-07T13:12:23.682714Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:23.398239Z","title":"Simplifying it with Assumption 3 and 4, Σ1 (V,e) = E[W 2 e ] + κe limp 1 p E[∥η − βe∥2 Σe] = (τ ∗ e )2","venue":null,"work_id":"fe0d010f-0b5d-43aa-b4cf-77064209e939","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.805261Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:76e91b6ec300c848d81293509d173c94108411be8455d69f0de9fa9528a5ef53","observation_id":"cdc153ff-7c1f-4639-b822-6a276e5b0329","resolution":{"observed_at":"2026-08-07T13:12:23.483273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:23.228980Z","title":null,"venue":null,"work_id":"43aa41ae-b2ae-4535-ba98-b5b841108e4d","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.857797Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:b3b5005ee7c17ed284b105b75df1850c6585836f3fd70eae6b81e611191f8434","observation_id":"d18802a1-006e-463c-9ac8-13a7c1ccb5bd","resolution":{"observed_at":"2026-08-07T13:12:23.286739Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:23.073208Z","title":"For the diagnoal elements of Σ 2 (V,e) we have simplified it in the same way as Σ 1 (V,e)","venue":null,"work_id":"68f05386-55c9-4423-829c-7937ebf212bd","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.926929Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:203a668793491ae0183ee55c2d6510a5ceefc28ba8aebeca0abe68f037bf37bb","observation_id":"707a456d-0343-4386-9bec-a4f8855a1af9","resolution":{"observed_at":"2026-08-07T13:12:23.154748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:22.870435Z","title":"We plan to show ρt,t+1 e converges exponentially fast to 1 for each e ∈ [E] with an argument of fixed point iteration","venue":null,"work_id":"a6cd6bf4-ed83-4c62-8a57-1ad5f7f6a98f","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:16.998312Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:1175f19da5618ec9036a423008b615effe10d8a66d0654fe9157fc3e32442bb7","observation_id":"dc954685-3fe9-4f0b-a54b-37c7e4486126","resolution":{"observed_at":"2026-08-07T13:12:22.966802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:22.646433Z","title":"We use the first line of Equation (C-23) to bound 1 p ∥∆η(2)∥2","venue":null,"work_id":"bf36bfd6-d0cc-42f5-a95e-b757ceb8deef","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.111653Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:3922faf1e866049275e1a53dedbccfffca4a166005c798b92938552cce06fe5f","observation_id":"4d18a84a-d77d-4d1a-926f-370ef76814c6","resolution":{"observed_at":"2026-08-07T13:12:22.736310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:22.472930Z","title":"Since ∆ η(2) = ∆ηt − ∆η(1), we know 1 p ∥diag(⃗λSc)(∆η(2))Sc∥1 − 1 p [diag(⃗λSc)st Sc]⊤(∆η(2))Sc ≤ ϵ2 · c2 2c4 3 + 4 √ 2ϵc2c3, where we have used the fact that M > 1 from (i)","venue":null,"work_id":"43341aee-2095-4b7e-91d1-5fbbc1878f1c","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.179565Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:9303b115ff221ef0b5d424191783b014876f67ff963ec19f7604f5a5ba45b6cb","observation_id":"020b8edb-2137-455c-985f-fb34c2164be9","resolution":{"observed_at":"2026-08-07T13:12:22.556209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:22.212180Z","title":null,"venue":null,"work_id":"ba338e8e-0805-4574-90f5-d5faff51f53d","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.238092Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:ac1f6e79c596951bef313516c598e4f7e4dedce337ea2bd2463284ed4008bf54","observation_id":"43bb8588-cec5-4379-a751-152f83b9ac73","resolution":{"observed_at":"2026-08-07T13:12:22.328569Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:21.994945Z","title":"Simplifying it with Assumption 5 and 6, Σ1 (ind,V,e) = E[W 2 e ] + κe limp 1 p E[∥ηe − βe∥2 Σ(ind,e) ] = (τ ∗ ind,e)2","venue":null,"work_id":"d4aaf1dd-c32c-4b2a-92a2-ea1adb8bcd58","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.305753Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:fe49ac690d5d90b302cfbf329bf749d6409ac396aa0aa21cedfb59d77b3a5401","observation_id":"8ff7836f-6587-49b4-87e0-10f3f9794559","resolution":{"observed_at":"2026-08-07T13:12:22.077655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:21.752823Z","title":null,"venue":null,"work_id":"25f7d99e-7763-4d86-8c4e-497e595afaf3","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.372858Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:aeb66f662275b6a969043e53da87eb48ad0944471f3faa622c59452cc549eeb7","observation_id":"59da4bb3-82b9-4fff-bb99-b20f73f6b074","resolution":{"observed_at":"2026-08-07T13:12:21.834179Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:21.598965Z","title":"For the diagnoal elements of Σ 2 (ind,V,e) we have simplified it in the same way as Σ 1 (ind,V,e)","venue":null,"work_id":"fde4a5f8-28b3-40c1-b5ce-bdc2c1d09cfe","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.439223Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:1314bd64f21f0b2481b4a359ac65e5bf85c8a7107b82e3d95eaef7186f75a22a","observation_id":"7d580dfb-658e-4cee-8797-da3528fed958","resolution":{"observed_at":"2026-08-07T13:12:21.635275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:21.402921Z","title":null,"venue":null,"work_id":"304eef3d-417b-4a3c-a717-28e3529f6452","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.492969Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:4d744510c93f7713e9216c9a9ca890eb3c0c6f8919557d4b2e145162387e49a6","observation_id":"165f5a2f-ba25-407c-8d8e-e8a193863c42","resolution":{"observed_at":"2026-08-07T13:12:21.509991Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:21.191174Z","title":"We are left to verify that the state evolution is well-defined, and satisfies the marginal properties 68 in Lemma E.2","venue":null,"work_id":"30695529-012d-41ab-9608-0549e8bc38a7","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.548777Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:328ea8c74edccc2ae8312232cd8b2f72f99c19932fae3bc02f2833eb5faa67ba","observation_id":"e895b8bb-76bf-44e3-b2d0-a2008df23d3e","resolution":{"observed_at":"2026-08-07T13:12:21.282155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:20.960828Z","title":"71 By the law of iterated expectations, HII(1) ≥ HII(0)","venue":null,"work_id":"6e7d7925-19c1-428f-87e7-f425227555b0","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.609492Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:c1b1a0ff15dbf6563c5d0eddd8526ea9764f344805f9678cae033bc822fe2fa1","observation_id":"35ae32ca-2eb2-4877-9453-7c4fa02cf335","resolution":{"observed_at":"2026-08-07T13:12:21.086669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:20.765710Z","title":"Then sII ∈ ∇µII(ξ; η), or in the case of the joint estimator, sII/λII ∈ ∂∥ξ − η∥1","venue":null,"work_id":"0c8deca7-6715-486a-9d5a-edf8bd80cfda","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.687582Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:91bb8c6309597490db32b2ee4c636690823b537c592d18c8bbb9994557c6795f","observation_id":"873b849b-b331-47c8-a7cd-05b3e110dd9e","resolution":{"observed_at":"2026-08-07T13:12:20.851399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:20.570014Z","title":null,"venue":null,"work_id":"8622a855-ddcf-457b-bef8-911cbded70ad","year":null},"citing_paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:17.757071Z"},"links":{"citing_paper":"/paper/2505.22594"},"observation_digest":"sha256:76c1fcbee6144a0d136b7b4ab3250e67d5daf1c2eed1fee3ca1e80b3a9e44de0","observation_id":"a56e30e0-3d36-4483-a9b5-c392964d65d9","resolution":{"observed_at":"2026-08-07T13:12:20.654646Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.22594","last_updated":"2025-06-29T10:07:14Z","latest_version":2,"primary_category":"math.ST","snapshot_observed_at":"2026-08-07T13:01:06.475365Z","submitted_at":"2025-05-28T17:05:09Z","title":"Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":7,"verified_fuzzy":53},"total_outbound_references":102},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 1 inbound Pith citation observation for arXiv:2505.22594."}