{"total":19,"items":[{"citing_arxiv_id":"2606.25494","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting","primary_cat":"math.PR","submitted_at":"2026-06-24T07:22:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22850","ref_index":94,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"To select or not to select: predictively consistent priors instead of model selection","primary_cat":"stat.ME","submitted_at":"2026-06-22T04:52:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.15464","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Certified Finite-Shot Operating Windows for Virtual Distillation and Symmetry Verification","primary_cat":"quant-ph","submitted_at":"2026-06-13T20:42:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10767","ref_index":69,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Two-Sample Homogeneity Test via Entropic Optimal Transport","primary_cat":"stat.ME","submitted_at":"2026-06-09T12:21:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes and analyzes a homogeneity test using squared L2 distance of empirical EOT maps to uniform-on-ball reference, with FCLT, Gaussian quadratic null limit, consistency, local power, and weighted multiplier bootstrap.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04237","ref_index":41,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Constrained Weighted Bayesian Bootstrap","primary_cat":"stat.ME","submitted_at":"2026-06-02T21:37:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04128","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On prediction-powered inference for quantile regression via convolution smoothing","primary_cat":"stat.ME","submitted_at":"2026-06-02T18:36:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03656","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Point Estimates: Reliable Evaluation of Prediction Performance Metrics under Clustered Data","primary_cat":"stat.ME","submitted_at":"2026-06-02T13:45:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Develops a unified framework representing performance metrics as smooth functionals of confusion-matrix probabilities to enable cluster-robust sandwich variance estimation for asymptotically valid confidence intervals and tests under clustered data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27928","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Experimental Collapse in Virophysics: Protocol-Resolved Observation, Inference, and Plaque-Assay Blindness","primary_cat":"physics.bio-ph","submitted_at":"2026-05-27T03:58:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper introduces a protocol-resolved framework for virological measurements, defining an observation operator that maps latent ensembles to observed data and recasting plaque assays as estimates of protocol-conditioned infectious concentration.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22883","ref_index":42,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems","primary_cat":"cs.AI","submitted_at":"2026-05-20T22:55:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes EpG and OOI metrics showing agentic workflows use 4.33x more energy per successful goal than linear baselines due to orchestration structure.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13710","ref_index":46,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Pattern-based tests for two-dimensional copulas","primary_cat":"math.ST","submitted_at":"2026-05-13T15:57:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"discrete uniform marginals, and may be interpreted as a discrete copula, or as an empirical copula𝐶𝑛 if rescaled by 1/𝑛, and the convergence of𝐶𝑛 to the copula𝐶associated with𝜇is of interest. A standard approach regards𝐶 𝑛 and𝐶as functions on the unit square, and related Glivenko-Cantelli type results and functional central limit theorems have been investigated by several authors; see e.g. [22] and [46]. If the empirical process associated with the original data is the main starting point then𝐶is required to satisfy some smoothness condition; see [21]. We approach the underlying distribution on the unit square not through its distribution function but through its pattern probabilities, in rough analogy to the representation of a distribution on the"},{"citing_arxiv_id":"2605.08034","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Semiparametric Efficient Test for Interpretable Distributional Treatment Effects","primary_cat":"stat.ML","submitted_at":"2026-05-08T17:23:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.","context_count":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"[35] Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet. Universality, characteristic kernels and rkhs embedding of measures.Journal of Machine Learning Research, 12(7), 2011. [36] A. W. van der Vaart.Asymptotic Statistics, volume 3 ofCambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998. doi: 10.1017/CBO9780511802256. [37] Jean-Philippe Vert. Classification of biological sequences with kernel methods. In Yasubumi Sakakibara, Satoshi Kobayashi, Kengo Sato, Tetsuro Nishino, and Etsuji Tomita, editors,Grammatical Inference: Algorithms and Applications, volume 4201 ofLecture Notes in Computer Science, pages 7-18, Berlin, Heidelberg, 2006. Springer. doi: 10.1007/11872436_2."},{"citing_arxiv_id":"2605.07920","ref_index":38,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Primitive Sequences for Probability Measures on Compact Intervals","primary_cat":"math.PR","submitted_at":"2026-05-08T15:55:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25368","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Self-organized regime switching in null-recurrent dynamics","primary_cat":"math.ST","submitted_at":"2026-04-28T08:33:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Profile MLE for the regime-switching threshold in null-recurrent diffusion converges at rate n^{-(1+γ)/2} to the arg sup of a doubly stochastic drifted Poisson process involving local time of oscillating Brownian motion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25154","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Prior-Aligned Data Cleaning for Tabular Foundation Models","primary_cat":"cs.LG","submitted_at":"2026-04-28T02:56:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"van der Vaart. 1998.Asymptotic Statistics. Cambridge University Press. doi:10.1017/CBO9780511802256 [29] S.M. Xie, A. Raghunathan, P. Liang, and T. Ma. 2022. An Explanation of In-Context Learning as Implicit Bayesian Inference. InProceedings of the International Con- ference on Learning Representations (ICLR). https://openreview.net/forum?id= RdJVFCHjUMI [30] H. Ye, S. Liu, H. Cai, Q. Zhou, and D. Zhan. 2024. A Closer Look at Deep Learn- ing Methods on Tabular Datasets. InNeurIPS Workshop on Table Representation Learning. https://arxiv.org/abs/2407.00956 [31] D. Zha, Z.P. Bhat, K.H. Lai, F. Yang, Z. Jiang, S. Zhong, and X. Hu. 2023. Data- Centric Artificial Intelligence: A Survey.arXiv preprint arXiv:2303."},{"citing_arxiv_id":"2602.01629","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.10245","ref_index":48,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Kernel Treatment Effects with Adaptively Collected Data","primary_cat":"stat.ML","submitted_at":"2025-10-11T15:01:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.01942","ref_index":19,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control","primary_cat":"math.OC","submitted_at":"2025-08-03T22:31:05+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Central limit theorems are established for SAA value functions in finite-horizon stochastic optimal control via an abstract limit theorem for stochastic backward recursions, yielding recursive asymptotic variance formulas under unique optimal policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.15036","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Extreme Value Analysis based on Blockwise Top-Two Order Statistics","primary_cat":"math.ST","submitted_at":"2025-02-20T20:45:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2403.08966","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming","primary_cat":"math.OC","submitted_at":"2024-03-13T21:12:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}