{"total":10,"items":[{"citing_arxiv_id":"2605.17126","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety","primary_cat":"stat.ML","submitted_at":"2026-05-16T19:06:54+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08773","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Prediction-Powered Linear Regression: A Balance Between Interpretation and Prediction","primary_cat":"stat.ME","submitted_at":"2026-05-09T07:58:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07285","ref_index":9,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Transporting treatment effects by calibrating large-scale observational outcomes","primary_cat":"stat.ME","submitted_at":"2026-05-08T05:48:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Eobs[ψ(∆(X))]we define Σ = ¯α⊤Erct \u0002 (D−¯µ(X))2ψ(∆(X))ψ(∆(X)) ⊤\u0003 ¯α. If additionally∥ψ j ◦ ˆ∆−ψ j ◦∆∥ 2+ϵ,rct =o p(1)for someϵ >0and allj= 1, . . . , pwith Erct[|D|4]<∞,E rct[∥ψ(∆(X))∥ k]<∞for allk <∞andψ( ˆ∆(X))having finite moments of all orders in the RCT with probability tending to 1, meaning that lim inf Pr \u0012Z ψ( ˆ∆(x)) k frct(x)dλ(x)<∞for allk <∞ \u0013 = 1,(9) then the confidence interval covers asymptotically: lim inf Pr \u0012 ˆ¯τ−z1−α/2 q ˆVn,N ⩽τ⩽ ˆ¯τ+z1−α/2 q ˆVn,N \u0013 ⩾1−α,∀α∈(0,1). In the crop rotation data, we haven < N 1/2. In this case, the rate condition∥ ˆ∆− ∆∥2,obs =o p(n−1/2) is satisfied as long as ∆ can be estimated at a root mean square rate faster thano p(N −1/4). This is the standard double machine learning rate, and immediately"},{"citing_arxiv_id":"2605.05076","ref_index":106,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","primary_cat":"math.ST","submitted_at":"2026-05-06T16:11:09+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03282","ref_index":201,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens","primary_cat":"stat.ME","submitted_at":"2026-05-05T02:14:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27892","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Prediction-powered Inference by Mixture of Experts","primary_cat":"stat.ML","submitted_at":"2026-04-30T14:08:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An MOE-powered PPI framework adaptively blends multiple predictors to achieve minimal variance and a best-expert guarantee for semi-supervised mean estimation, linear regression, quantile estimation, and M-estimation, supported by non-asymptotic coverage bounds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21260","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Calibeating Prediction-Powered Inference","primary_cat":"stat.ML","submitted_at":"2026-04-23T04:06:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21020","ref_index":54,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience","primary_cat":"stat.ME","submitted_at":"2026-04-22T19:08:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18569","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Revisiting Active Sequential Prediction-Powered Mean Estimation","primary_cat":"stat.ML","submitted_at":"2026-04-20T17:55:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Non-asymptotic analysis of prediction-powered mean estimation shows that no-regret learning for query probabilities converges to the maximum allowed constant value, independent of covariates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.06452","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap","primary_cat":"math.ST","submitted_at":"2025-05-09T21:58:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}