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 45
Two-Level Softmax Sampling Done Right: Correcting Bias from Size Imbalance and Dispersion
Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampling impractical at scale.Best Arm Identification for Bandits with Shifting Means
We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means.Oracle-Efficient and Parameter-Free Agnostic Smoothed Online Learning
Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially.Why Forget-Only Unlearning Needs Memorization
Machine unlearning asks for a deletion algorithm whose output is close to retraining from scratch without the selected forget examples.JevForest: Path Voting for Budgeted Feature Acquisition
Choosing which information to observe is central to prediction under limited observation budgets.Exact SO(3)-Equivariant Isotropic Kernels for Rotation-Robust Neural Dynamics
Neural surrogates for vector-valued partial differential equations can fit training data yet change their predictions when the same physical state is expressed in a rotated coordinate frame.D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition
Compressing a matrix for reconstruction still defaults to the truncated SVD, approximating the data with a single low-rank structure.How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise.From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier
This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the model.Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Understanding why deep neural networks make a given prediction is of great importance for their safe deployment.What can linear attention learn from nonlinear teachers in-context?
Linear attention is a tractable model for understanding the mechanisms governing in-context learning in transformers.Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and…Conformal Prediction under Partial Verification
Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive.Transformed Samplers with Variance Reduction
Markov chain Monte Carlo (MCMC) methods are the standard tool for computing expectations under complex probability distributions.SPD-MetaFormer is what you need for small-data brain decoding
Brain signal decoding is challenging because neural recordings are noisy and vary across individuals, while labeled data are often limited.Is $\sqrt{d}$ Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?
Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in machine learning.Learning a Mixture of GFlowNets
Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space exploration and convergence than that of a monolithic…Vine Copula VAR:From Recursive Margins to Joint Forecast Inference
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions.Explicit Asymptotic Bounds for Sequential Calibration Beyond $T^{2/3}$
Probability forecasts are calibrated when predicted probabilities match empirical outcome frequencies: among events assigned a probability $p$, we'd hope that the fraction of positive outcomes is…Asymptotic Analysis of Empirical Risk Minimization on Entry-wise i.i.d. Heavy-Tailed Data
Many real-world datasets exhibit unusually large values far more frequently than predicted by Gaussian models.