arXiv:2509.11379v1 Announce Type: new Abstract: We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk…
arXiv:2509.11338v1 Announce Type: new Abstract: We present a simple and scalable implementation of next-generation reservoir computing for modeling dynamical systems from time series data. Our approach uses a pseudorandom nonlinear projection of time-delay embedded input, allowing an arbitrary dimension of…
arXiv:2101.09875v3 Announce Type: replace-cross Abstract: This work studies the spectral convergence of graph Laplacian to the Laplace-Beltrami operator when the graph affinity matrix is constructed from $N$ random samples on a $d$-dimensional manifold embedded in a possibly high dimensional space.…
arXiv:2506.19010v2 Announce Type: replace Abstract: Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs).…
arXiv:2508.12426v2 Announce Type: replace-cross Abstract: The minimum density power divergence estimator (MDPDE) has gained significant attention in the literature of robust inference due to its strong robustness properties and high asymptotic efficiency; it is relatively easy to compute and can…
arXiv:2509.12031v1 Announce Type: cross Abstract: In this paper, we examine the problem of sampling from log-concave distributions with (possibly) superlinear gradient growth under kinetic (underdamped) Langevin algorithms. Using a carefully tailored taming scheme, we propose two novel discretizations of the…
arXiv:2509.11070v1 Announce Type: new Abstract: We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework encompasses two key classes: (i) compact kernels, which admit discrete spectral decompositions, and (ii) diagonal…
arXiv:2509.12154v1 Announce Type: cross Abstract: The first part of this paper studies the evolution of gradient flow for homogeneous neural networks near a class of saddle points exhibiting a sparsity structure. The choice of these saddle points is motivated from…
arXiv:2509.10531v1 Announce Type: new Abstract: Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset allocation using trial-and-error interactions. However,…
arXiv:2508.07982v2 Announce Type: replace-cross Abstract: We propose a novel kernel-based nonparametric two-sample test, employing the combined use of kernel mean and kernel covariance embedding. Our test builds on recent results showing how such combined embeddings map distinct probability measures to…
