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 3
Point interpolation and distributional universality in flow-based generative models
Motivated by the expressive power of neural ordinary differential equations in flow-based generative modeling, we study the approximation of probability measures by pushforwards generated by…Two-Layer Random ReLU Networks Can Separate Disjoint Classes
Set separation by continuous linear functionals is fundamental in functional analysis and convex optimization and underlies binary classification in machine learning.OmniST: Topology-Invariant Spatiotemporal Forecasting with Guaranteed-Coverage Uncertainty at Unseen Sensor Locations
Standard spatiotemporal graph convolutional networks need a fixed-size adjacency matrix, coupling model weights to a specific sensor count N; they cannot produce any output at a sensor absent from…How Risky Is Your Data-Driven Policy?
Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing their \emph{average} performance.Nearly Linear-Time Follow-the-Perturbed-Leader for Weakly Observable Graph Bandits
We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Leader (FTPL) policy that achieves best-of-both-worlds guarantee with nearly linear…Pairwise Distortion Distribution with Graph-Based Importance Sampling for Quantization Error and Intrinsic Dimension Estimation
Quantifying the dimensionality of data is a fundamental need in machine learning and signal processing applications such as representation learning, data compression, and inverse problems, where the…Proper Agnostic Learning of Affine Halfspaces under Log-Concave Marginals
We give a proper constant-factor agnostic learner for affine halfspaces under full-dimensional log-concave marginals.A Bayesian-Geometric Reading of Classical Model-Based Optimal Design
The classical model-based optimal design criteria (D-, T-, L-, C-, and A-optimality) are usually motivated via the Cramér--Rao bound as optimizing scalar functions of the Fisher information matrix…Conditional independence and graphical models for rankings
Ranking models define probability distributions over rankings and are fundamental in applications such as preference learning and decision-making.Directional Statistics in Signal Processing: A Geometry Driven Overview of Models, Learning, and Applications
Many signal-processing objects are defined by periodicity, normalization, sign or global-phase ambiguity, basis invariance, or positive-semidefinite constraints and therefore cannot be modeled…One-shot Distributed Learning via Weighted Averaging with Server-side Information
Distributed learning has drawn considerable attention due to its capability of learning from distributed data without aggregating raw observations.Where Is the Truth When AI Agents Disagree? Dependence-Aware Belief Propagation for Unsupervised Truth Discovery (Extended Version)
LLM agents often give conflicting answers to the same question, and many deployed agents are variants of one base model, so their errors are correlated.UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation
Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without…Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks
Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology.Exact Selective Inference for Spectral Clustering via Piecewise-Constant Graph Perturbation
Cluster labels are often used twice: first to discover groups, and then to test whether the discovered groups differ.Beyond Multi-Index Models: Learning Intersections of Degree-2 PTFs
We give an algorithm for PAC learning intersections of $k$ degree-2 polynomial threshold functions over $R^d$ under a Gaussian distribution.A Random Matrix Analysis of Symmetric Tensor Unfolding
We study a fundamental problem in machine learning: partial recovery of a planted signal $\boldsymbol{v} \in \mathbb{S}^{n-1}$ from a spiked tensor $\boldsymbol{\mathsf{X}} = λ\…Macrocanonical Generator Networks: Data-Efficient Neural Surrogates for Amortized Physics Simulation
Many scientific simulations are computationally expensive, limiting their use in simulation-based inference, uncertainty quantification, and decision making.Privacy for Free in Efficient Agnostic Halfspace Learning
We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces.Logarithmic Regret for Alternating Mirror Descent
Alternating online mirror descent (AltOMD) is a simple learning rule for zero-sum games, which improves upon simultaneous OMD numerically and theoretically.