{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AUEJJVT5X6AKCY5YNG3CX6QSGE","short_pith_number":"pith:AUEJJVT5","canonical_record":{"source":{"id":"1912.01698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-12-03T21:52:38Z","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"02d7661b305ca4a58940cba5083dc4a543e74aae5722565f4dc99ee7e0847bf3","abstract_canon_sha256":"7b90eb7d155e6de6c6bdb2046e10cf9a6407562ffd8670b7ffd0e788715c5f08"},"schema_version":"1.0"},"canonical_sha256":"050894d67dbf80a163b869b62bfa12310385ea5f461df32c1ad2e3289301b160","source":{"kind":"arxiv","id":"1912.01698","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.01698","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"arxiv_version","alias_value":"1912.01698v1","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.01698","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_12","alias_value":"AUEJJVT5X6AK","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_16","alias_value":"AUEJJVT5X6AKCY5Y","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_8","alias_value":"AUEJJVT5","created_at":"2026-07-05T00:23:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AUEJJVT5X6AKCY5YNG3CX6QSGE","target":"record","payload":{"canonical_record":{"source":{"id":"1912.01698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-12-03T21:52:38Z","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"02d7661b305ca4a58940cba5083dc4a543e74aae5722565f4dc99ee7e0847bf3","abstract_canon_sha256":"7b90eb7d155e6de6c6bdb2046e10cf9a6407562ffd8670b7ffd0e788715c5f08"},"schema_version":"1.0"},"canonical_sha256":"050894d67dbf80a163b869b62bfa12310385ea5f461df32c1ad2e3289301b160","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:23:53.263722Z","signature_b64":"keUlYhcLXnaYeyx8PppwkdmAt9SfxtO5VNrPq8z6AHd3tzUi4jqlYAh1H+24EeCq6ETDegTCFP4DX9eGlVuxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"050894d67dbf80a163b869b62bfa12310385ea5f461df32c1ad2e3289301b160","last_reissued_at":"2026-07-05T00:23:53.263297Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:23:53.263297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.01698","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":"2026-07-05T00:23:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MfA6mbR9Mru1bMWeepMTSHUIDz8J3F043Q+6ZMCMT+qdafCuDUPn3wf5E8G3j5r0OejFj9NNOOSSACH/Wu4gBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:54:21.090582Z"},"content_sha256":"8e71d1eec762c19e17eb3a89e6230c7b03ef05f17e8e07dd25ae55ce534572c0","schema_version":"1.0","event_id":"sha256:8e71d1eec762c19e17eb3a89e6230c7b03ef05f17e8e07dd25ae55ce534572c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AUEJJVT5X6AKCY5YNG3CX6QSGE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"primary_cat":"math.OC","authors_text":"Abhishek Roy, Krishnakumar Balasubramanian, Prasant Mohapatra, Yifang Chen","submitted_at":"2019-12-03T21:52:38Z","abstract_excerpt":"Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available for saddle-point optimization has focused on the offline setting. In this paper, we study nonstationary versions of stochastic, smooth, strongly-convex and strongly-concave saddle-point optimization problem, in both online (or first-order) and multi-point bandit (or zeroth-order) settings. We first propose natural notions of regret for such nonstationary saddle-point optimization"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.01698","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/1912.01698/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":"2026-07-05T00:23:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UCiplqqtEFAVr2uY0x2IxgRU771icMMHTs6uTdMtWBqTdA/nQjIdXeax1WLwGnG0gM5LU15Q60FOFImKa9GnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:54:21.091539Z"},"content_sha256":"708d7dd2fbb8208db2d2d3d55072ce4ea0b9d6a0f763b0738e60af2d7420c148","schema_version":"1.0","event_id":"sha256:708d7dd2fbb8208db2d2d3d55072ce4ea0b9d6a0f763b0738e60af2d7420c148"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/bundle.json","state_url":"https://pith.science/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/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":"2026-08-18T19:54:21Z","links":{"resolver":"https://pith.science/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE","bundle":"https://pith.science/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/bundle.json","state":"https://pith.science/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AUEJJVT5X6AKCY5YNG3CX6QSGE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AUEJJVT5X6AKCY5YNG3CX6QSGE","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":"7b90eb7d155e6de6c6bdb2046e10cf9a6407562ffd8670b7ffd0e788715c5f08","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-12-03T21:52:38Z","title_canon_sha256":"02d7661b305ca4a58940cba5083dc4a543e74aae5722565f4dc99ee7e0847bf3"},"schema_version":"1.0","source":{"id":"1912.01698","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.01698","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"arxiv_version","alias_value":"1912.01698v1","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.01698","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_12","alias_value":"AUEJJVT5X6AK","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_16","alias_value":"AUEJJVT5X6AKCY5Y","created_at":"2026-07-05T00:23:53Z"},{"alias_kind":"pith_short_8","alias_value":"AUEJJVT5","created_at":"2026-07-05T00:23:53Z"}],"graph_snapshots":[{"event_id":"sha256:708d7dd2fbb8208db2d2d3d55072ce4ea0b9d6a0f763b0738e60af2d7420c148","target":"graph","created_at":"2026-07-05T00:23:53Z","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/1912.01698/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available for saddle-point optimization has focused on the offline setting. In this paper, we study nonstationary versions of stochastic, smooth, strongly-convex and strongly-concave saddle-point optimization problem, in both online (or first-order) and multi-point bandit (or zeroth-order) settings. We first propose natural notions of regret for such nonstationary saddle-point optimization","authors_text":"Abhishek Roy, Krishnakumar Balasubramanian, Prasant Mohapatra, Yifang Chen","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-12-03T21:52:38Z","title":"Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.01698","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":"sha256:8e71d1eec762c19e17eb3a89e6230c7b03ef05f17e8e07dd25ae55ce534572c0","target":"record","created_at":"2026-07-05T00:23:53Z","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":"7b90eb7d155e6de6c6bdb2046e10cf9a6407562ffd8670b7ffd0e788715c5f08","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-12-03T21:52:38Z","title_canon_sha256":"02d7661b305ca4a58940cba5083dc4a543e74aae5722565f4dc99ee7e0847bf3"},"schema_version":"1.0","source":{"id":"1912.01698","kind":"arxiv","version":1}},"canonical_sha256":"050894d67dbf80a163b869b62bfa12310385ea5f461df32c1ad2e3289301b160","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"050894d67dbf80a163b869b62bfa12310385ea5f461df32c1ad2e3289301b160","first_computed_at":"2026-07-05T00:23:53.263297Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:23:53.263297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"keUlYhcLXnaYeyx8PppwkdmAt9SfxtO5VNrPq8z6AHd3tzUi4jqlYAh1H+24EeCq6ETDegTCFP4DX9eGlVuxCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:23:53.263722Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.01698","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e71d1eec762c19e17eb3a89e6230c7b03ef05f17e8e07dd25ae55ce534572c0","sha256:708d7dd2fbb8208db2d2d3d55072ce4ea0b9d6a0f763b0738e60af2d7420c148"],"state_sha256":"8211f7e734e59164ba50af09220febb316eb6610a64f98ad2c6bfb966321628f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fkDuTgwFC5DA6yiPkwP7oxYs68tCbSJVCtZIqZxAEDZ/a+KzwGaMjfn+Ce3X55UXU8NzgFnvp+CH4jX2fuBPDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T19:54:21.101255Z","bundle_sha256":"fb0cfb8699e2463e0f3721ffd4d34745ca6340770e621ec1252f09404990dc75"}}