{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:P4Q6ADWO3GCOX2TS32S42PHDBB","short_pith_number":"pith:P4Q6ADWO","schema_version":"1.0","canonical_sha256":"7f21e00eced984ebea72dea5cd3ce3087b81481cc54b1f5035ea5704eb224721","source":{"kind":"arxiv","id":"2401.15771","version":5},"attestation_state":"computed","paper":{"title":"Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nhat Ho, Nicola Bariletto","submitted_at":"2024-01-28T21:19:15Z","abstract_excerpt":"Training machine learning and statistical models often involves optimizing a data-driven risk criterion. The risk is usually computed with respect to the empirical data distribution, but this may result in poor and unstable out-of-sample performance due to distributional uncertainty. In the spirit of distributionally robust optimization, we propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. First, we highlight novel connections with standard regularized"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.15771","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-01-28T21:19:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"07b0d5d482379382bc36d21263c32d3aa64b284a4e21d161c1c72dd9e3d73adc","abstract_canon_sha256":"8ba8ed63ed6b8cb9d190a67e41823cdcb95fc5a6a57a48f10bb51e7f7df7458c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:41.760125Z","signature_b64":"by1SUsFOWSBke/djeBBnpVmsjVw7oKI/DYSNkSWOTt7DhRxHRqeEKo8AxDSaCKXfUcUEDbxcSgyphgzE5/zeCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f21e00eced984ebea72dea5cd3ce3087b81481cc54b1f5035ea5704eb224721","last_reissued_at":"2026-07-05T09:32:41.759700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:41.759700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nhat Ho, Nicola Bariletto","submitted_at":"2024-01-28T21:19:15Z","abstract_excerpt":"Training machine learning and statistical models often involves optimizing a data-driven risk criterion. The risk is usually computed with respect to the empirical data distribution, but this may result in poor and unstable out-of-sample performance due to distributional uncertainty. In the spirit of distributionally robust optimization, we propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. First, we highlight novel connections with standard regularized"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15771","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.15771/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.15771","created_at":"2026-07-05T09:32:41.759762+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.15771v5","created_at":"2026-07-05T09:32:41.759762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15771","created_at":"2026-07-05T09:32:41.759762+00:00"},{"alias_kind":"pith_short_12","alias_value":"P4Q6ADWO3GCO","created_at":"2026-07-05T09:32:41.759762+00:00"},{"alias_kind":"pith_short_16","alias_value":"P4Q6ADWO3GCOX2TS","created_at":"2026-07-05T09:32:41.759762+00:00"},{"alias_kind":"pith_short_8","alias_value":"P4Q6ADWO","created_at":"2026-07-05T09:32:41.759762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.16829","citing_title":"Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB","json":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB.json","graph_json":"https://pith.science/api/pith-number/P4Q6ADWO3GCOX2TS32S42PHDBB/graph.json","events_json":"https://pith.science/api/pith-number/P4Q6ADWO3GCOX2TS32S42PHDBB/events.json","paper":"https://pith.science/paper/P4Q6ADWO"},"agent_actions":{"view_html":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB","download_json":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB.json","view_paper":"https://pith.science/paper/P4Q6ADWO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.15771&json=true","fetch_graph":"https://pith.science/api/pith-number/P4Q6ADWO3GCOX2TS32S42PHDBB/graph.json","fetch_events":"https://pith.science/api/pith-number/P4Q6ADWO3GCOX2TS32S42PHDBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB/action/storage_attestation","attest_author":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB/action/author_attestation","sign_citation":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB/action/citation_signature","submit_replication":"https://pith.science/pith/P4Q6ADWO3GCOX2TS32S42PHDBB/action/replication_record"}},"created_at":"2026-07-05T09:32:41.759762+00:00","updated_at":"2026-07-05T09:32:41.759762+00:00"}