| { |
| "schema_version": 1, |
| "title": "Reproduction: Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality", |
| "emoji": "🧮", |
| "space_id": "snaykey/repro-profiling-irrational-agent", |
| "paper": { |
| "openreview_id": "5hDvooOKUP" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-5hDvooOKUP" |
| ], |
| "updated_at": "2026-07-31T00:00:00Z", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-thm35-cluster-recovery", |
| "title": "The adaptive orthogonal multitask estimator achieves exact recovery of the latent task clustering with high probability, established via Theorem 3.5 (Section 3, Cluster Recovery).", |
| "file": "pages/claim-1-thm35-cluster-recovery/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-thm35-pooled-rate", |
| "title": "For tasks in cluster k with pooled sample size N_k, the estimator attains the rate ||θ̂_j − θ*_j||_2 = O_P(N_k^{-1/2}), matching pooled parametric convergence (Theorem 3.5, Section 3).", |
| "file": "pages/claim-2-thm35-pooled-rate/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-thm36-asymptotic-normality", |
| "title": "√N_k(θ̂_j − θ*_j) is asymptotically normal with covariance matching the oracle estimator that knows the true clustering in advance (Theorem 3.6, Section 3).", |
| "file": "pages/claim-3-thm36-asymptotic-normality/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-thm37-38-within-cluster-heterogeneity", |
| "title": "Extensions permit within-cluster heterogeneity bounded by ξ_k = O(N_k^{-1/2}) while preserving the estimation guarantees (Theorems 3.7–3.8, Section 3).", |
| "file": "pages/claim-4-thm37-38-within-cluster-heterogeneity/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-sec44-simulations-ari", |
| "title": "In simulations across three models (PLM, ATE, DID) and separation levels δ ∈ {1/3, 2/3, 1}, the method achieves Adjusted Rand Index (ARI) near 1, outperforming competing clustering approaches (Section 4.4).", |
| "file": "pages/claim-5-sec44-simulations-ari/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6-sec5-recs-real-data", |
| "title": "On real electricity price elasticity data from 51 US states, the method recovers three clusters, e.g. Virginia at −1.138 ± 0.189 versus a 46-state cluster at −0.221 ± 0.009 (Table 1, Section 5).", |
| "file": "pages/claim-6-sec5-recs-real-data/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| } |
| } |
