{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:EZO6MTN6PDO4CRRLATSKVBN2UJ","short_pith_number":"pith:EZO6MTN6","canonical_record":{"source":{"id":"2505.16829","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T16:01:49Z","cross_cats_sorted":["cs.DS","cs.GT"],"title_canon_sha256":"82747794badbf6273ad4d69f8497a3532c3536eb758052f021e517d35343c0a0","abstract_canon_sha256":"ece85ab45846a346a9f349bc44149b24866ee1d0d0fc7a3fb844df49db269443"},"schema_version":"1.0"},"canonical_sha256":"265de64dbe78ddc1462b04e4aa85baa2755b6293c33e2cfc86811994b197d8af","source":{"kind":"arxiv","id":"2505.16829","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.16829","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.16829v1","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16829","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"EZO6MTN6PDO4","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"EZO6MTN6PDO4CRRL","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"EZO6MTN6","created_at":"2026-07-05T11:07:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:EZO6MTN6PDO4CRRLATSKVBN2UJ","target":"record","payload":{"canonical_record":{"source":{"id":"2505.16829","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T16:01:49Z","cross_cats_sorted":["cs.DS","cs.GT"],"title_canon_sha256":"82747794badbf6273ad4d69f8497a3532c3536eb758052f021e517d35343c0a0","abstract_canon_sha256":"ece85ab45846a346a9f349bc44149b24866ee1d0d0fc7a3fb844df49db269443"},"schema_version":"1.0"},"canonical_sha256":"265de64dbe78ddc1462b04e4aa85baa2755b6293c33e2cfc86811994b197d8af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:41.904666Z","signature_b64":"HwvJTMGLXSBOtylAV0NJPp9KsvtRf+JqOE4+A1za/asBFeAZ3iZu2zbipLTMs+2M6SwZINCMY8psNvphgYLrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"265de64dbe78ddc1462b04e4aa85baa2755b6293c33e2cfc86811994b197d8af","last_reissued_at":"2026-07-05T11:07:41.904153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:41.904153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.16829","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"41utVofdRx1PM+L4f6gIgSENzkwsqw2h15ujaxjPgJaD4xXvh4io9+XPa2IdKy9w7wyZBoohI43waBUeHgUyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:39:02.204998Z"},"content_sha256":"b567545fdfd496a6dc2939968eaa445a66885c8957d723604f52360de22278a6","schema_version":"1.0","event_id":"sha256:b567545fdfd496a6dc2939968eaa445a66885c8957d723604f52360de22278a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:EZO6MTN6PDO4CRRLATSKVBN2UJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contextual Learning for Stochastic Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS","cs.GT"],"primary_cat":"cs.LG","authors_text":"Anna Heuser, Thomas Kesselheim","submitted_at":"2025-05-22T16:01:49Z","abstract_excerpt":"Motivated by stochastic optimization, we introduce the problem of learning from samples of contextual value distributions. A contextual value distribution can be understood as a family of real-valued distributions, where each sample consists of a context $x$ and a random variable drawn from the corresponding real-valued distribution $D_x$. By minimizing a convex surrogate loss, we learn an empirical distribution $D'_x$ for each context, ensuring a small L\\'evy distance to $D_x$. We apply this result to obtain the sample complexity bounds for the learning of an $\\epsilon$-optimal policy for sto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16829","kind":"arxiv","version":1},"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/2505.16829/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ud+fXOrl6rPZV3ybjm6/PqHCJbgcD7X7RlyOx5jxwUGOp6VOOwIvF5PBkQBgr935SmjEDV2+i1BIjf120h8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:39:02.205906Z"},"content_sha256":"a9e8db7e4c0b3dda8c426e4c16d83fe97d2f05ac116ab69d5c2ae8d5fcad6cad","schema_version":"1.0","event_id":"sha256:a9e8db7e4c0b3dda8c426e4c16d83fe97d2f05ac116ab69d5c2ae8d5fcad6cad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/bundle.json","state_url":"https://pith.science/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/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":"2026-08-20T12:39:02Z","links":{"resolver":"https://pith.science/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ","bundle":"https://pith.science/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/bundle.json","state":"https://pith.science/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EZO6MTN6PDO4CRRLATSKVBN2UJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EZO6MTN6PDO4CRRLATSKVBN2UJ","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":"ece85ab45846a346a9f349bc44149b24866ee1d0d0fc7a3fb844df49db269443","cross_cats_sorted":["cs.DS","cs.GT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T16:01:49Z","title_canon_sha256":"82747794badbf6273ad4d69f8497a3532c3536eb758052f021e517d35343c0a0"},"schema_version":"1.0","source":{"id":"2505.16829","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.16829","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.16829v1","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16829","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"EZO6MTN6PDO4","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"EZO6MTN6PDO4CRRL","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"EZO6MTN6","created_at":"2026-07-05T11:07:41Z"}],"graph_snapshots":[{"event_id":"sha256:a9e8db7e4c0b3dda8c426e4c16d83fe97d2f05ac116ab69d5c2ae8d5fcad6cad","target":"graph","created_at":"2026-07-05T11:07:41Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.16829/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Motivated by stochastic optimization, we introduce the problem of learning from samples of contextual value distributions. A contextual value distribution can be understood as a family of real-valued distributions, where each sample consists of a context $x$ and a random variable drawn from the corresponding real-valued distribution $D_x$. By minimizing a convex surrogate loss, we learn an empirical distribution $D'_x$ for each context, ensuring a small L\\'evy distance to $D_x$. We apply this result to obtain the sample complexity bounds for the learning of an $\\epsilon$-optimal policy for sto","authors_text":"Anna Heuser, Thomas Kesselheim","cross_cats":["cs.DS","cs.GT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16829","kind":"arxiv","version":1},"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:b567545fdfd496a6dc2939968eaa445a66885c8957d723604f52360de22278a6","target":"record","created_at":"2026-07-05T11:07:41Z","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":"ece85ab45846a346a9f349bc44149b24866ee1d0d0fc7a3fb844df49db269443","cross_cats_sorted":["cs.DS","cs.GT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T16:01:49Z","title_canon_sha256":"82747794badbf6273ad4d69f8497a3532c3536eb758052f021e517d35343c0a0"},"schema_version":"1.0","source":{"id":"2505.16829","kind":"arxiv","version":1}},"canonical_sha256":"265de64dbe78ddc1462b04e4aa85baa2755b6293c33e2cfc86811994b197d8af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"265de64dbe78ddc1462b04e4aa85baa2755b6293c33e2cfc86811994b197d8af","first_computed_at":"2026-07-05T11:07:41.904153Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:07:41.904153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HwvJTMGLXSBOtylAV0NJPp9KsvtRf+JqOE4+A1za/asBFeAZ3iZu2zbipLTMs+2M6SwZINCMY8psNvphgYLrAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:07:41.904666Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.16829","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b567545fdfd496a6dc2939968eaa445a66885c8957d723604f52360de22278a6","sha256:a9e8db7e4c0b3dda8c426e4c16d83fe97d2f05ac116ab69d5c2ae8d5fcad6cad"],"state_sha256":"16d25bcea669286d1e8099811fda58b0a5cafa0354ffe165f106ee76c98f08e8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/naGlSWkSquxVmwU1TUBvpKcJo0O/CfiDjkF5NIeFWwGqBYpNHTcqrHpWgK/ObUrgOwoMd7WwVM8LYzC62FmCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T12:39:02.211513Z","bundle_sha256":"9fe2f2dc49c076a65837c1593a3ce48cea04815d0b78be325cc2dbe4aeb9a11b"}}