{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:LO7VD3MMOLS565OTMVJH3TXCH3","short_pith_number":"pith:LO7VD3MM","canonical_record":{"source":{"id":"1510.02706","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-09T15:31:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67b3d56fa8aa255824e9559114e8ad9e78b163afa98ee4ebdd2b27a610152a55","abstract_canon_sha256":"f64442ccff353e76c16bf7c3f85308c5619534e12228f1fc48a8c8cd7bc6edec"},"schema_version":"1.0"},"canonical_sha256":"5bbf51ed8c72e5df75d365527dcee23efc34811163f09e2331c8c06a2ebfaab3","source":{"kind":"arxiv","id":"1510.02706","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.02706","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"arxiv_version","alias_value":"1510.02706v2","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.02706","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"pith_short_12","alias_value":"LO7VD3MMOLS5","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_16","alias_value":"LO7VD3MMOLS565OT","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_8","alias_value":"LO7VD3MM","created_at":"2026-05-18T12:29:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:LO7VD3MMOLS565OTMVJH3TXCH3","target":"record","payload":{"canonical_record":{"source":{"id":"1510.02706","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-09T15:31:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67b3d56fa8aa255824e9559114e8ad9e78b163afa98ee4ebdd2b27a610152a55","abstract_canon_sha256":"f64442ccff353e76c16bf7c3f85308c5619534e12228f1fc48a8c8cd7bc6edec"},"schema_version":"1.0"},"canonical_sha256":"5bbf51ed8c72e5df75d365527dcee23efc34811163f09e2331c8c06a2ebfaab3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:11.644984Z","signature_b64":"nEHsJ26+ymla7JTO+oTLIMB+FXHkUFB38G5J6nRJdfMipuOEBFunraM8O8M9wkhmkFLxETDc9FEUDushSGA3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bbf51ed8c72e5df75d365527dcee23efc34811163f09e2331c8c06a2ebfaab3","last_reissued_at":"2026-05-18T01:19:11.644402Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:11.644402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.02706","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:19:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P3IpMBB4hGOd7xS4d56af6Xiws99vKxBR6NWyyuqivoBh3TCEXYv5tXVT026QLKgZ76CGRnsv/z/dRTMnD+iDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:01:23.360573Z"},"content_sha256":"c0d8cbae05c194937bbe070a54368a6934b24c0623edb7f0baa3f2ab74f71109","schema_version":"1.0","event_id":"sha256:c0d8cbae05c194937bbe070a54368a6934b24c0623edb7f0baa3f2ab74f71109"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:LO7VD3MMOLS565OTMVJH3TXCH3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditional Risk Minimization for Stochastic Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alexander Zimin, Christoph H. Lampert","submitted_at":"2015-10-09T15:31:36Z","abstract_excerpt":"We study the task of learning from non-i.i.d. data. In particular, we aim at learning predictors that minimize the conditional risk for a stochastic process, i.e. the expected loss of the predictor on the next point conditioned on the set of training samples observed so far. For non-i.i.d. data, the training set contains information about the upcoming samples, so learning with respect to the conditional distribution can be expected to yield better predictors than one obtains from the classical setting of minimizing the marginal risk. Our main contribution is a practical estimator for the condi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.02706","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:19:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bUMARwB1tK6qUv4HtNoGdHWoV6DjaCmxwpveYIbpAn7v7dXIC01iUeUYwSvEK+hflsf8TRT8IPNsnEGPAnFODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:01:23.361260Z"},"content_sha256":"22f5c99c42efdc438b5606bdef4f03a1bf412f338cc7889d274fcd90e5879d43","schema_version":"1.0","event_id":"sha256:22f5c99c42efdc438b5606bdef4f03a1bf412f338cc7889d274fcd90e5879d43"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LO7VD3MMOLS565OTMVJH3TXCH3/bundle.json","state_url":"https://pith.science/pith/LO7VD3MMOLS565OTMVJH3TXCH3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LO7VD3MMOLS565OTMVJH3TXCH3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T16:01:23Z","links":{"resolver":"https://pith.science/pith/LO7VD3MMOLS565OTMVJH3TXCH3","bundle":"https://pith.science/pith/LO7VD3MMOLS565OTMVJH3TXCH3/bundle.json","state":"https://pith.science/pith/LO7VD3MMOLS565OTMVJH3TXCH3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LO7VD3MMOLS565OTMVJH3TXCH3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:LO7VD3MMOLS565OTMVJH3TXCH3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f64442ccff353e76c16bf7c3f85308c5619534e12228f1fc48a8c8cd7bc6edec","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-09T15:31:36Z","title_canon_sha256":"67b3d56fa8aa255824e9559114e8ad9e78b163afa98ee4ebdd2b27a610152a55"},"schema_version":"1.0","source":{"id":"1510.02706","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.02706","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"arxiv_version","alias_value":"1510.02706v2","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.02706","created_at":"2026-05-18T01:19:11Z"},{"alias_kind":"pith_short_12","alias_value":"LO7VD3MMOLS5","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_16","alias_value":"LO7VD3MMOLS565OT","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_8","alias_value":"LO7VD3MM","created_at":"2026-05-18T12:29:29Z"}],"graph_snapshots":[{"event_id":"sha256:22f5c99c42efdc438b5606bdef4f03a1bf412f338cc7889d274fcd90e5879d43","target":"graph","created_at":"2026-05-18T01:19:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We study the task of learning from non-i.i.d. data. In particular, we aim at learning predictors that minimize the conditional risk for a stochastic process, i.e. the expected loss of the predictor on the next point conditioned on the set of training samples observed so far. For non-i.i.d. data, the training set contains information about the upcoming samples, so learning with respect to the conditional distribution can be expected to yield better predictors than one obtains from the classical setting of minimizing the marginal risk. Our main contribution is a practical estimator for the condi","authors_text":"Alexander Zimin, Christoph H. Lampert","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-09T15:31:36Z","title":"Conditional Risk Minimization for Stochastic Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.02706","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c0d8cbae05c194937bbe070a54368a6934b24c0623edb7f0baa3f2ab74f71109","target":"record","created_at":"2026-05-18T01:19:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f64442ccff353e76c16bf7c3f85308c5619534e12228f1fc48a8c8cd7bc6edec","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-09T15:31:36Z","title_canon_sha256":"67b3d56fa8aa255824e9559114e8ad9e78b163afa98ee4ebdd2b27a610152a55"},"schema_version":"1.0","source":{"id":"1510.02706","kind":"arxiv","version":2}},"canonical_sha256":"5bbf51ed8c72e5df75d365527dcee23efc34811163f09e2331c8c06a2ebfaab3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5bbf51ed8c72e5df75d365527dcee23efc34811163f09e2331c8c06a2ebfaab3","first_computed_at":"2026-05-18T01:19:11.644402Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:19:11.644402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nEHsJ26+ymla7JTO+oTLIMB+FXHkUFB38G5J6nRJdfMipuOEBFunraM8O8M9wkhmkFLxETDc9FEUDushSGA3Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:19:11.644984Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.02706","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0d8cbae05c194937bbe070a54368a6934b24c0623edb7f0baa3f2ab74f71109","sha256:22f5c99c42efdc438b5606bdef4f03a1bf412f338cc7889d274fcd90e5879d43"],"state_sha256":"f030bc85de8e0e47c857ccf8bd033901600fc2e43f0fdf55a988ce064c0929a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qPzEaJaT2pWVh6Rjd3gaQLtHgsxK5Gkd5b7JY53Th34jp/x69o82CGo0mpiSkfheYjv2VH2rPLm10bA817dNCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T16:01:23.364898Z","bundle_sha256":"de3e47b676449878ad8967e91ddf393b3af11bcae2221df3f6a167765a2bbf62"}}