arXiv:2402.05689v4 Announce Type: replace
Abstract: We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward t…
arXiv:2603.26940v1 Announce Type: cross
Abstract: The optimal transportation problem defines a geometry of probability measures which leads to a definition for weighted averages (barycenters) of measures, finding application in the machine learning an…
arXiv:2603.27019v1 Announce Type: cross
Abstract: This study addresses the inverse problem of parameter estimation for Stochastic Differential Equations (SDEs) by minimizing a regularized discrepancy functional via Stochastic Gradient Descent (SGD). T…
arXiv:2603.25934v1 Announce Type: cross
Abstract: In this work, we investigate how to develop sharp concentration inequalities for sub-Weibull random variables, including sub-Gaussian and sub-exponential distributions. Although the random variables ma…
arXiv:2603.26301v1 Announce Type: cross
Abstract: Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, …
arXiv:2603.26231v1 Announce Type: new
Abstract: Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental cha…
arXiv:2603.26217v1 Announce Type: cross
Abstract: Generalized Hopfield models with higher-order or exponential interaction terms
are known to have substantially larger storage capacities than the classical
quadratic model. On the other hand, assoc…
arXiv:2603.23783v2 Announce Type: new
Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncer…
arXiv:2602.20844v2 Announce Type: replace-cross
Abstract: Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate…
arXiv:2603.23783v1 Announce Type: new
Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncer…