| { |
| "schema_version": 1, |
| "title": "Reproduction: Gradient Flow Sampler-based Distributionally Robust Optimization", |
| "emoji": "🌊", |
| "space_id": "ProCreations/repro-gradient-flow-sampler-based-distributionally-robust-optimization", |
| "paper": { |
| "arxiv_id": "2510.25956" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-QRtzkKrbJi" |
| ], |
| "updated_at": "2026-07-23T15:58:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Gradient Flow Sampler-based Distributionally Robust Optimization", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-six-algorithms", |
| "title": "Claim 1: The paper introduces a unified PDE gradient flow framework for distributionally robust optimization (DRO) with six concrete algorithms, including Wasserstein Gradient Flow (Algorithm 3) and Wasserstein Fisher-Rao flow (Algorithm 4) variants for entropy-regularized Wasserstein DRO (Section 4, Algorithms 3-4).", |
| "file": "pages/claim-1-six-algorithms/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-flow-time", |
| "title": "Claim 2: Proposition 1 shows the Wasserstein gradient flow sampler must run for time at least on the order of O((1/λ) log(L/√(λε))) to produce an ε-accurate gradient estimate (Section 4, Proposition 1).", |
| "file": "pages/claim-2-flow-time/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-outer-loop", |
| "title": "Claim 3: Theorem 1 proves the outer loop of the gradient-flow-sampler-based DRO algorithm requires O(1/ε²_opt) iterations to reach an ε-stationary point (Section 5, Theorem 1).", |
| "file": "pages/claim-3-outer-loop/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-total-complexity", |
| "title": "Claim 4: Theorem 2 bounds the total computational complexity of the WGF-based DRO algorithm (Algorithm 3) as Õ(L_Φ L²_U L²_f d² / (λ³_U ε⁴_opt)) (Section 5, Theorem 2).", |
| "file": "pages/claim-4-total-complexity/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-cifar-robustness", |
| "title": "Claim 5: On CIFAR-10 adversarial training under PGD attacks, the WFR- and WGF-based DRO methods achieve consistently higher robust accuracy across all perturbation settings compared to baseline DRO methods (Section 6.3).", |
| "file": "pages/claim-5-cifar-robustness/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6-half-bridge", |
| "title": "Claim 6: Lemma 1 establishes that the entropy-regularized DRO problem is equivalent to a Schrödinger half-bridge problem, enabling sampling from the conditional worst-case distribution (Section 3.1, Lemma 1).", |
| "file": "pages/claim-6-half-bridge/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 4096 |
| } |
|
|
