| { |
| "schema_version": 1, |
| "title": "Repro: Optimal Regularization for Performative Learning", |
| "emoji": "chart", |
| "space_id": "snaykey/repro-optimal-reg-performative", |
| "paper": { |
| "openreview_id": "G4ve69pimc" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-G4ve69pimc" |
| ], |
| "updated_at": "2026-07-27T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Repro: Optimal Regularization for Performative Learning", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-population-excess-risk-characterization", |
| "title": "In the population setting, Theorem 1 characterizes excess risk as a function of the magnitude and direction of the performative effect together with spurious features (Section 4, Theorem 1).", |
| "file": "pages/claim-1-population-excess-risk-characterization/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-optimal-regularization-proportional", |
| "title": "Corollary 2 shows the optimal regularization parameter in the population regime is proportional to the strength of the performative effect, with optimal risk remaining strictly positive (Section 4, Corollary 2).", |
| "file": "pages/claim-2-optimal-regularization-proportional/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-deterministic-equivalent-fixed-point", |
| "title": "Theorem 3 establishes a deterministic equivalent of the performative fixed point for over-parameterized ridge regression when the number of features exceeds the number of samples (Section 5, Theorem 3).", |
| "file": "pages/claim-3-deterministic-equivalent-fixed-point/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-optimal-regularization-sign-flip-noise", |
| "title": "Theorem 4 shows the optimal regularization moves in the same direction as the performative effect on predictive features under low noise, but in the opposite direction under high noise, in the over-parameterized regime (Section 5, Theorem 4).", |
| "file": "pages/claim-4-optimal-regularization-sign-flip-noise/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-overparam-performativity-improves-risk", |
| "title": "Numerical experiments in Section 6 confirm that in the over-parameterized setting, performative effects can improve optimally-regularized risk when performativity reinforces existing trends, contrasting with the population-regime degradation (Section 6).", |
| "file": "pages/claim-5-overparam-performativity-improves-risk/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| } |
| } |
|
|
