AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
The newest papers in Machine Learning (Statistics) that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 11
Finite-Sample LTI System Identification from a Misspecified Finite Model Set
We consider the problem of finite-sample identification of an unknown LTI system from a finite hypothesis class.Rural Readiness: Domain Generalization with Adversarially Chosen Domains
Predictive models trained at well-resourced data-rich institutions often fail when deployed in new populations.Numerical Integration on Bounded Domains via Learned Reflected Diffusions
We study high-dimensional integration on bounded convex domains.Self-regularization and structural adaptation of NPMLE for Gaussian mixture models in all dimensions
We establish structural properties of the nonparametric maximum likelihood estimator (NPMLE) for $d$-dimensional Gaussian mixtures whose means lie in a hypercube.An ODE-based Sampler for Discrete Diffusion Models
The marginals of a continuous diffusion model can be reproduced by a deterministic ordinary differential equation (ODE), known as the probability flow ODE, which provides a foundation for…The Geometry of Multitask Learning: Sharp Rates for Shared Subspace Estimation
Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain poorly understood in many of the formal settings considered in the literature.INLA for Adaptive UAV Sampling of Radio Environment Maps in IoT Networks: Prior Width Governs Accuracy and Calibration
We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations.Certified Behavioral Retention for Continual Adaptation with Held-Out Anchors
Continual adaptation requires deciding not only how to update a model, but whether to commit an update without exceeding a declared budget for changes to protected behavior.Near-Optimal First-Order Regret for Bandits with Feedback Graphs
We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph.Robust-by-Design Distributional Learning from Contaminated Samples
Modern data analysis pipelines increasingly rely on large datasets assembled through retrieval, scraping, automated filtering, and post-hoc curation, where the observed sample is often a…Density-Guided Bootstrap Boundaries for Latent World-Model Reinforcement Learning
Latent world models allow reinforcement learning agents to learn policies from imagined trajectories, yet determining the optimal depth of model reliance remains a fundamental challenge.Omniprediction for All Mixable Proper Losses and Beyond
Omniprediction concerns the task of making predictions that are simultaneously accurate with respect to different losses.Where, When, and How Many Bits? Scaling Laws for SGD with Stochastic Rounding
Low-precision training reduces computation but introduces additional rounding error.MiniMMD: Minimization of MMD for Sampling, Inference and Integration
Many problems in computational statistics and machine learning can be formulated as approximating a target distribution by minimizing a discrepancy over a restricted class of candidate distributions.Estimating Implicit Expected Log Density Ratios
When exact probabilities cannot be computed, \emph{density ratio estimation (DRE)} is a key subroutine in the wide range of applications where density ratios appear, from experimental design to…Scalable Bayesian Quadrature via Operator-Adapted Features
Kernel methods face two recurrent bottlenecks: the cubic cost of inverting an $n\times n$ kernel matrix, as in kernel ridge regression, and the evaluation of kernel integrals, as required in kernel…Nearly Minimax Optimal Regret for Adversarial Linear Bandits with Strong Corruptions
We study adversarial linear bandits with strong corruption, in which an adaptive adversary chooses the reward vectors and a strong adversary corrupts each observed reward after seeing the learner's…Capacity of the Hopfield-Hebbian dynamics with correlated patterns
Allowing for small fraction of errors in retrieved stored binary patterns, Hopfield-Hebbian neural network (NN) associative memory dynamics [14] achieves linearly scaled capacity.A goodness of fit approach to select the optimal visit sequence for nonparametric data synthesis
Synthetic data are a promising way to maintain privacy while disseminating research data, thereby enabling compliance with the open science principles.On the Generalization Gap of Streaming Federated Learning with Markovian Data
Streaming federated learning (SFL) allows clients to train a shared model from continuously arriving data without retaining large local datasets.