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.
Get the few papers worth your time in Machine Learning (Statistics) every morning.
Follow Machine Learning (Statistics) and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 46
Uniform Discrete Diffusion Models are Minimax Optimal for Estimating Distributions with Small Effective Support Size
Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistical generalization properties remain poorly…Stability of Measure-to-Measure Transformers on Sub-Gaussian Data
Transformers have exhibited impressive empirical success across various domains, but their theoretical foundations remain less developed.Exact Calibration and Sharp Risk Geometry for Volume-Sampled Ridge Regression
We study ridge regression from exactly $s$ distinct rows of a fixed design.Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret
We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rewards.High-dimensional online calibration from harmonic weights
We study the online calibration of multidimensional forecasts over an arbitrary convex set $Y\subseteq\mathbb{R}^d$ relative to an arbitrary error norm $\|\cdot\|_{L}$.Adversarially Trained Linear Transformers Are Optimal Robust In-Context Learners for Gaussian Mixtures
Adversarial training is one of the most reliable defenses against adversarial attacks, but its high computational cost must generally be paid anew for each task.Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects
Large language models (LLMs) are increasingly used as judges for automated AI evaluation.Extending Pathwise Gradients to Discrete Random Variables via Finite-Order Relaxation
Pathwise gradients are preferred for continuous random variables because they are unbiased, low variance, and work with a single sample.Adaptive Mean Estimation by In-Context Learning: A Gradient-Flow Analysis
Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks.Stochastic Gradient Descent Ascent is Suboptimal for Nonconvex-PL Min-Max Games
How far can stochastic gradient descent ascent (SGDA) go by tuning its timescale ratio and step sizes in nonconvex min-max games?Spectra: Exact Component Transport for Test-Time Prior Adaptation in Simulation-Based Inference
Simulation-based inference (SBI) has become a powerful approach to Bayesian inference in complex scientific models whose likelihoods are difficult or impossible to evaluate.Detecting a Shift Is Not Enough: Exact Minimax Limits of Linear Representation Repair
A mean shift between two data sources can be easy to detect but hard to remove without substantially changing their representations.ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved.Beyond Marginal Monitoring: Distributed Joint-Distribution Testing for Data Concept Drift in Large Scale E-Commerce Operations
Concept drift threatens production machine learning, yet the empirical behavior of multivariate two-sample drift detectors at scale remains under-characterized.Anytime-valid simulation-based hypothesis testing
For a given data distribution $(X_t)_{t \in \mathbb{N}} \sim Q$ i.i.d., we investigate the hypothesis testing problem: $H_0: Q = P_0$ vs. $H_1: Q = P_1$, for two different model probability…How Many Independent Samples Does a Satellite Image Contain? Generalization Bounds for Spatially Dependent Data
Machine learning classifiers for remote sensing imagery are typically evaluated as though every pixel were an independent sample.Two-Sample Testing via Generative Processes
Deciding whether two samples come from the same distribution is a classical problem in statistics, and generative transport offers a new way to approach it.High-Dimensional Statistical Inference for Sparse Support Vector Machines
Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample size and number of features grow proportionally.Network Intervention by Polling Strategic Agents
A planner in a network of strategic agents faces three entangled challenges: the optimum depends on agents' private information, queried agents may misreport to steer the outcome, and exact…Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models
We establish a polynomial sample complexity separation between symmetry-aware and symmetry-agnostic feature learning.