{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:FCILL4EL2GZYXKHPYGKXFXRTGX","short_pith_number":"pith:FCILL4EL","canonical_record":{"source":{"id":"2010.08418","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T14:33:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"490f33e3080bcde6bd19e5a62f3b27daebe92523836505d900c865fcc14f81b3","abstract_canon_sha256":"71ede081f0fa2d5db87bbdfe6c281c9d418b15e1bcba7af087b64dd71bbcc137"},"schema_version":"1.0"},"canonical_sha256":"2890b5f08bd1b38ba8efc19572de3335f06b8d7e7f573df19bd05a3c325ae04c","source":{"kind":"arxiv","id":"2010.08418","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.08418","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"arxiv_version","alias_value":"2010.08418v1","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08418","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_12","alias_value":"FCILL4EL2GZY","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_16","alias_value":"FCILL4EL2GZYXKHP","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_8","alias_value":"FCILL4EL","created_at":"2026-07-05T01:43:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:FCILL4EL2GZYXKHPYGKXFXRTGX","target":"record","payload":{"canonical_record":{"source":{"id":"2010.08418","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T14:33:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"490f33e3080bcde6bd19e5a62f3b27daebe92523836505d900c865fcc14f81b3","abstract_canon_sha256":"71ede081f0fa2d5db87bbdfe6c281c9d418b15e1bcba7af087b64dd71bbcc137"},"schema_version":"1.0"},"canonical_sha256":"2890b5f08bd1b38ba8efc19572de3335f06b8d7e7f573df19bd05a3c325ae04c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:43:34.708960Z","signature_b64":"HIQZ0ZJDSLz/uWy9PepEceFxuqPYjyMtP2m/Pi5ik2PxKjZiaDI5g+iLKEByjGiRIdGZB1SkbAxVyXBstgg+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2890b5f08bd1b38ba8efc19572de3335f06b8d7e7f573df19bd05a3c325ae04c","last_reissued_at":"2026-07-05T01:43:34.708513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:43:34.708513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.08418","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":"2026-07-05T01:43:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vis7svSyx68wzwPPCalWJ5/OFWkW9L6K5cvHaKu1N7wUtF6UUvvp06xSPbiRKQABhzxNgjJXCdzYi2fwDOYzCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T01:33:59.965741Z"},"content_sha256":"fadafc1633eb7593d4f16892a8fc7af2a55d694634b494788cbbbbccc92213d8","schema_version":"1.0","event_id":"sha256:fadafc1633eb7593d4f16892a8fc7af2a55d694634b494788cbbbbccc92213d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:FCILL4EL2GZYXKHPYGKXFXRTGX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aranyak Mehta, Di Wang, D. Sivakumar, Goran Zuzic","submitted_at":"2020-10-16T14:33:11Z","abstract_excerpt":"We address the challenge of finding algorithms for online allocation (i.e. bipartite matching) using a machine learning approach. In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance. In contrast to existing work, our goal is to accomplish algorithm design {\\em tabula rasa}, i.e., without any human-provided insights or expert-tuned training data beyond specifying the objective and constraints of the optimization problem. We construct a framework based on insights and ideas from game theory, adversar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08418","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/2010.08418/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":"2026-07-05T01:43:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"asPHwC3GGoya5CoE+DxSBKNDvuU8M12bF28a/2PwIh1RbtG3lkUXdRwt15/D3jWU6UaNX9XJM/0oyNcitKUsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T01:33:59.966269Z"},"content_sha256":"3b80226a3fdf174a80fa6a26d77dfad92ff183ee9cdc885c1798640444dcf277","schema_version":"1.0","event_id":"sha256:3b80226a3fdf174a80fa6a26d77dfad92ff183ee9cdc885c1798640444dcf277"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/bundle.json","state_url":"https://pith.science/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/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":"2026-08-15T01:33:59Z","links":{"resolver":"https://pith.science/pith/FCILL4EL2GZYXKHPYGKXFXRTGX","bundle":"https://pith.science/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/bundle.json","state":"https://pith.science/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FCILL4EL2GZYXKHPYGKXFXRTGX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:FCILL4EL2GZYXKHPYGKXFXRTGX","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":"71ede081f0fa2d5db87bbdfe6c281c9d418b15e1bcba7af087b64dd71bbcc137","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T14:33:11Z","title_canon_sha256":"490f33e3080bcde6bd19e5a62f3b27daebe92523836505d900c865fcc14f81b3"},"schema_version":"1.0","source":{"id":"2010.08418","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.08418","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"arxiv_version","alias_value":"2010.08418v1","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08418","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_12","alias_value":"FCILL4EL2GZY","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_16","alias_value":"FCILL4EL2GZYXKHP","created_at":"2026-07-05T01:43:34Z"},{"alias_kind":"pith_short_8","alias_value":"FCILL4EL","created_at":"2026-07-05T01:43:34Z"}],"graph_snapshots":[{"event_id":"sha256:3b80226a3fdf174a80fa6a26d77dfad92ff183ee9cdc885c1798640444dcf277","target":"graph","created_at":"2026-07-05T01:43:34Z","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/2010.08418/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the challenge of finding algorithms for online allocation (i.e. bipartite matching) using a machine learning approach. In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance. In contrast to existing work, our goal is to accomplish algorithm design {\\em tabula rasa}, i.e., without any human-provided insights or expert-tuned training data beyond specifying the objective and constraints of the optimization problem. We construct a framework based on insights and ideas from game theory, adversar","authors_text":"Aranyak Mehta, Di Wang, D. Sivakumar, Goran Zuzic","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T14:33:11Z","title":"Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08418","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:fadafc1633eb7593d4f16892a8fc7af2a55d694634b494788cbbbbccc92213d8","target":"record","created_at":"2026-07-05T01:43:34Z","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":"71ede081f0fa2d5db87bbdfe6c281c9d418b15e1bcba7af087b64dd71bbcc137","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T14:33:11Z","title_canon_sha256":"490f33e3080bcde6bd19e5a62f3b27daebe92523836505d900c865fcc14f81b3"},"schema_version":"1.0","source":{"id":"2010.08418","kind":"arxiv","version":1}},"canonical_sha256":"2890b5f08bd1b38ba8efc19572de3335f06b8d7e7f573df19bd05a3c325ae04c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2890b5f08bd1b38ba8efc19572de3335f06b8d7e7f573df19bd05a3c325ae04c","first_computed_at":"2026-07-05T01:43:34.708513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:43:34.708513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HIQZ0ZJDSLz/uWy9PepEceFxuqPYjyMtP2m/Pi5ik2PxKjZiaDI5g+iLKEByjGiRIdGZB1SkbAxVyXBstgg+BA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:43:34.708960Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.08418","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fadafc1633eb7593d4f16892a8fc7af2a55d694634b494788cbbbbccc92213d8","sha256:3b80226a3fdf174a80fa6a26d77dfad92ff183ee9cdc885c1798640444dcf277"],"state_sha256":"795db2a561b5b8483a057a3e1a634c282bc9033c0839b5b3333cb6673f8e934a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fcRE1ZF80RCbo+MNnNvtKwfYb9zTkd4PiZ/VNhtCqfX8XXioR9q2lJ3AHjhy2hNZwtRrQutVePaIL1BX0vO+Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T01:33:59.972668Z","bundle_sha256":"bcd6871a92d95fc2c2a93bcf99c0783c0e5fcb7041ae6a7bbe9f534d54e9ba58"}}