arXiv:2505.19635v3 Announce Type: replace
Abstract: Concentration of distances in high dimension is an important factor for the development and design of stable and reliable data analysis algorithms. In this paper, we address the fundamental long-stan…
arXiv:2603.28956v1 Announce Type: cross
Abstract: The minimum-norm interpolator (MNI) framework has recently attracted considerable attention as a tool for understanding generalization in overparameterized models, such as neural networks. In this work…
arXiv:2509.13130v3 Announce Type: replace-cross
Abstract: Prediction sets offer a binary inclusion/exclusion for each element at the same fixed confidence level. We generalize to fuzzy prediction sets, which exclude elements at their own data-driven c…
arXiv:2510.22351v2 Announce Type: replace-cross
Abstract: We study the problem of estimating the average treatment effect (ATE) under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, we con…
arXiv:2603.27370v1 Announce Type: cross
Abstract: This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimation…
arXiv:2603.27457v1 Announce Type: cross
Abstract: Motivated by applications in statistics and machine learning, we consider a problem of unmixing convex combinations of nonparametric densities. Suppose we observe $n$ groups of samples, where the $i$th…
arXiv:2507.12581v2 Announce Type: replace-cross
Abstract: While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty at the individ…
arXiv:2603.28359v1 Announce Type: cross
Abstract: This paper studies high-dimensional M-estimation in the proportional asymptotic regime (p/n -> gamma > 0) when the noise distribution has infinite variance. For noise with regularly-varying tails of in…
arXiv:2603.26954v1 Announce Type: new
Abstract: The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and then synchronize across workers. Additionally, recent wor…
arXiv:2211.01512v3 Announce Type: replace
Abstract: Motivated by the increasingly popular Score-based Generative Modeling (SGM), we study the Inexact Langevin Dynamics (ILD) and Inexact Langevin Algorithm (ILA) where a score function estimate is used …