{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:AZQ763KFOBY7B3M4GEX2XE22ZT","short_pith_number":"pith:AZQ763KF","schema_version":"1.0","canonical_sha256":"0661ff6d457071f0ed9c312fab935accf767da69d25ea6668f7c46deba30ef85","source":{"kind":"arxiv","id":"1606.03966","version":2},"attestation_state":"computed","paper":{"title":"Making Contextual Decisions with Low Technical Debt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Alekh Agarwal, Alex Slivkins, Dan Melamed, Gal Oshri, Jiaji Li, John Langford, Luong Hoang, Markus Cozowicz, Oswaldo Ribas, Sarah Bird, Siddhartha Sen, Stephen Lee","submitted_at":"2016-06-13T14:17:00Z","abstract_excerpt":"Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service.\n  Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggabili"},"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":"1606.03966","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-13T14:17:00Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"fabd5ab5fc12a235fabc5d363bf77b86928e38536bdbe72d5657ee593884e6fa","abstract_canon_sha256":"92cdf5e4e988626297296e18ace86f96cf87a9ebbf2ca6d1f346e63321a0b741"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:52.460742Z","signature_b64":"ewys7YuZ8P6Cg4ou3nSeL3eZYXab3w6iHPZIctEDtKwVMcv98w6JNndXP8V0YuJB3ocNSlUR5BegagGFKxgpCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0661ff6d457071f0ed9c312fab935accf767da69d25ea6668f7c46deba30ef85","last_reissued_at":"2026-05-18T00:44:52.460045Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:52.460045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Making Contextual Decisions with Low Technical Debt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Alekh Agarwal, Alex Slivkins, Dan Melamed, Gal Oshri, Jiaji Li, John Langford, Luong Hoang, Markus Cozowicz, Oswaldo Ribas, Sarah Bird, Siddhartha Sen, Stephen Lee","submitted_at":"2016-06-13T14:17:00Z","abstract_excerpt":"Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service.\n  Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggabili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03966","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1606.03966","created_at":"2026-05-18T00:44:52.460146+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.03966v2","created_at":"2026-05-18T00:44:52.460146+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03966","created_at":"2026-05-18T00:44:52.460146+00:00"},{"alias_kind":"pith_short_12","alias_value":"AZQ763KFOBY7","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"AZQ763KFOBY7B3M4","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"AZQ763KF","created_at":"2026-05-18T12:30:07.202191+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT","json":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT.json","graph_json":"https://pith.science/api/pith-number/AZQ763KFOBY7B3M4GEX2XE22ZT/graph.json","events_json":"https://pith.science/api/pith-number/AZQ763KFOBY7B3M4GEX2XE22ZT/events.json","paper":"https://pith.science/paper/AZQ763KF"},"agent_actions":{"view_html":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT","download_json":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT.json","view_paper":"https://pith.science/paper/AZQ763KF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.03966&json=true","fetch_graph":"https://pith.science/api/pith-number/AZQ763KFOBY7B3M4GEX2XE22ZT/graph.json","fetch_events":"https://pith.science/api/pith-number/AZQ763KFOBY7B3M4GEX2XE22ZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT/action/storage_attestation","attest_author":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT/action/author_attestation","sign_citation":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT/action/citation_signature","submit_replication":"https://pith.science/pith/AZQ763KFOBY7B3M4GEX2XE22ZT/action/replication_record"}},"created_at":"2026-05-18T00:44:52.460146+00:00","updated_at":"2026-05-18T00:44:52.460146+00:00"}