{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:EZKCZDTU6XEY673M7NLNSD3UFT","short_pith_number":"pith:EZKCZDTU","schema_version":"1.0","canonical_sha256":"26542c8e74f5c98f7f6cfb56d90f742cca37312894b75b650f83dc774249ed28","source":{"kind":"arxiv","id":"1711.00141","version":2},"attestation_state":"computed","paper":{"title":"Training GANs with Optimism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Ilyas, Constantinos Daskalakis, Haoyang Zeng, Vasilis Syrgkanis","submitted_at":"2017-10-31T23:09:08Z","abstract_excerpt":"We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics. Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs. We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynam"},"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":"1711.00141","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-31T23:09:08Z","cross_cats_sorted":["cs.GT","stat.ML"],"title_canon_sha256":"98ab0c0ac6e30cbcacd3ee2abf9411810f3efce17662913c413760f0c2a8dcdf","abstract_canon_sha256":"df6f64d84f018cf57f653e74ef163cfa694f692e33078d86d0f17261984be64b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:45.278958Z","signature_b64":"J3M5mNz81bZXILgKxgieOtuPA87o4O1KSyHamvgb8Ytq1ERjDo+Oa06l+8V1BAvKTK/NaKpznqeI16YhtiCqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26542c8e74f5c98f7f6cfb56d90f742cca37312894b75b650f83dc774249ed28","last_reissued_at":"2026-05-18T00:23:45.278436Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:45.278436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Training GANs with Optimism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Ilyas, Constantinos Daskalakis, Haoyang Zeng, Vasilis Syrgkanis","submitted_at":"2017-10-31T23:09:08Z","abstract_excerpt":"We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics. Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs. We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00141","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":"1711.00141","created_at":"2026-05-18T00:23:45.278514+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.00141v2","created_at":"2026-05-18T00:23:45.278514+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00141","created_at":"2026-05-18T00:23:45.278514+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZKCZDTU6XEY","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZKCZDTU6XEY673M","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZKCZDTU","created_at":"2026-05-18T12:31:12.930513+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2605.22644","citing_title":"Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics","ref_index":143,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16262","citing_title":"Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18809","citing_title":"Metric-Gradient Projection for Stable Multi-Agent Policy Learning","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2506.10874","citing_title":"Higher-Order Uncoupled Learning Dynamics and Nash Equilibrium","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10373","citing_title":"Shuffling the Data, Stretching the Step-size: Sharper Bias in constant step-size SGD","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT","json":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT.json","graph_json":"https://pith.science/api/pith-number/EZKCZDTU6XEY673M7NLNSD3UFT/graph.json","events_json":"https://pith.science/api/pith-number/EZKCZDTU6XEY673M7NLNSD3UFT/events.json","paper":"https://pith.science/paper/EZKCZDTU"},"agent_actions":{"view_html":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT","download_json":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT.json","view_paper":"https://pith.science/paper/EZKCZDTU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.00141&json=true","fetch_graph":"https://pith.science/api/pith-number/EZKCZDTU6XEY673M7NLNSD3UFT/graph.json","fetch_events":"https://pith.science/api/pith-number/EZKCZDTU6XEY673M7NLNSD3UFT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT/action/storage_attestation","attest_author":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT/action/author_attestation","sign_citation":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT/action/citation_signature","submit_replication":"https://pith.science/pith/EZKCZDTU6XEY673M7NLNSD3UFT/action/replication_record"}},"created_at":"2026-05-18T00:23:45.278514+00:00","updated_at":"2026-05-18T00:23:45.278514+00:00"}