{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:EI4AZDKJHZXH5RNAPV4XVWAOGN","short_pith_number":"pith:EI4AZDKJ","schema_version":"1.0","canonical_sha256":"22380c8d493e6e7ec5a07d797ad80e336531e736799d12e2f06c33243be3a9ad","source":{"kind":"arxiv","id":"2111.11207","version":2},"attestation_state":"computed","paper":{"title":"Improved Sample Complexity Bounds for Branch-and-Cut","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DS","math.OC"],"primary_cat":"cs.LG","authors_text":"Ellen Vitercik, Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm","submitted_at":"2021-11-18T04:07:29Z","abstract_excerpt":"Branch-and-cut is the most widely used algorithm for solving integer programs, employed by commercial solvers like CPLEX and Gurobi. Branch-and-cut has a wide variety of tunable parameters that have a huge impact on the size of the search tree that it builds, but are challenging to tune by hand. An increasingly popular approach is to use machine learning to tune these parameters: using a training set of integer programs from the application domain at hand, the goal is to find a configuration with strong predicted performance on future, unseen integer programs from the same domain. If the train"},"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":"2111.11207","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-18T04:07:29Z","cross_cats_sorted":["cs.AI","cs.DS","math.OC"],"title_canon_sha256":"4326b302d1e815c2af9c7c868ead73b4f3eb8856f61d518bc6abd31a0167426c","abstract_canon_sha256":"cd939eb59f696df1dfa97e160ce1e9c45b8ebf870b7f78ae3c0471dfddba59bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:39.405495Z","signature_b64":"zJQiBELgqLQEKlPqTWzfhY8HJXYAZqtYzNJxb84gYfTmA1P9qzLspcpRg1P9XNexX22RjDnqw0KeAdcvcvrzDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22380c8d493e6e7ec5a07d797ad80e336531e736799d12e2f06c33243be3a9ad","last_reissued_at":"2026-07-05T04:22:39.405019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:39.405019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Sample Complexity Bounds for Branch-and-Cut","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DS","math.OC"],"primary_cat":"cs.LG","authors_text":"Ellen Vitercik, Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm","submitted_at":"2021-11-18T04:07:29Z","abstract_excerpt":"Branch-and-cut is the most widely used algorithm for solving integer programs, employed by commercial solvers like CPLEX and Gurobi. Branch-and-cut has a wide variety of tunable parameters that have a huge impact on the size of the search tree that it builds, but are challenging to tune by hand. An increasingly popular approach is to use machine learning to tune these parameters: using a training set of integer programs from the application domain at hand, the goal is to find a configuration with strong predicted performance on future, unseen integer programs from the same domain. If the train"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.11207","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.11207/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":"2111.11207","created_at":"2026-07-05T04:22:39.405077+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.11207v2","created_at":"2026-07-05T04:22:39.405077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11207","created_at":"2026-07-05T04:22:39.405077+00:00"},{"alias_kind":"pith_short_12","alias_value":"EI4AZDKJHZXH","created_at":"2026-07-05T04:22:39.405077+00:00"},{"alias_kind":"pith_short_16","alias_value":"EI4AZDKJHZXH5RNA","created_at":"2026-07-05T04:22:39.405077+00:00"},{"alias_kind":"pith_short_8","alias_value":"EI4AZDKJ","created_at":"2026-07-05T04:22:39.405077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07239","citing_title":"Sample Complexity of Stochastic Optimization with Integer Variables","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN","json":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN.json","graph_json":"https://pith.science/api/pith-number/EI4AZDKJHZXH5RNAPV4XVWAOGN/graph.json","events_json":"https://pith.science/api/pith-number/EI4AZDKJHZXH5RNAPV4XVWAOGN/events.json","paper":"https://pith.science/paper/EI4AZDKJ"},"agent_actions":{"view_html":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN","download_json":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN.json","view_paper":"https://pith.science/paper/EI4AZDKJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.11207&json=true","fetch_graph":"https://pith.science/api/pith-number/EI4AZDKJHZXH5RNAPV4XVWAOGN/graph.json","fetch_events":"https://pith.science/api/pith-number/EI4AZDKJHZXH5RNAPV4XVWAOGN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN/action/storage_attestation","attest_author":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN/action/author_attestation","sign_citation":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN/action/citation_signature","submit_replication":"https://pith.science/pith/EI4AZDKJHZXH5RNAPV4XVWAOGN/action/replication_record"}},"created_at":"2026-07-05T04:22:39.405077+00:00","updated_at":"2026-07-05T04:22:39.405077+00:00"}