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 35
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality
We study online individualized treatment selection when only the outcome under the assigned treatment is observed.Locally adaptive step sizes for Hamiltonian Monte Carlo methods
Exact reversible locally adaptive step sizes are introduced by Bou-Rabee et al. (2026) in the context of the No-U-Turn Hamiltonian Monte Carlo (HMC) sampler for the purpose of high performance HMC…Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features
We study the last iterate of stochastic gradient descent (SGD) on streaming least squares with discretely distributed features (Slock's model), which decouples the dynamics across eigendirections.Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization
In a variety of multi-agent and physical environments, an element's update depends strongly on a few other elements and weakly on many, and both the identity and the number of the elements that…$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows
Accurately assessing the convergence of Monte Carlo Markov Chains is crucial for ensuring their validity in Bayesian inference problems.Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate
Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; post-hoc temperature scaling corrects calibration…Quickest Change-point Detection in Generalized Linear Models with Variational Estimation
We consider quickest change-point detection in generalized linear models when the post-change parameter is unknown.Amortized Variational Inference of Latent Intensities from Replicated Point Patterns
Bayesian inference for latent point process intensities often relies on per-dataset sampling or optimization, making repeated inference computationally expensive.Averaged Annealed Markov Chains Achieve the Algorithmic Threshold for Tensor PCA, Part I: Glauber Dynamics
In the tensor principal components analysis (tensor PCA) problem, we are asked to recover an unknown vector in $\{\pm1\}^N$ from a noisy observation of its $k$-th tensor power, for a fixed $k\ge3$.Amortised adaptive importance sampling
Monte Carlo sampling algorithms aim to produce samples from a target distribution with a given unnormalised probability density function.One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought
Chain-of-Thought (CoT) enables transformers to solve complex reasoning tasks by generating intermediate reasoning steps.When Repeated Measurements Hurt Tabular Foundation Models
Tabular foundation models (TFMs) achieve state-of-the-art performance on IID benchmarks, but how do they respond to repeated measurements at the same inputs?On the Calibration of Crowdsourcing Aggregators
Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly used for making probabilistic decisions.Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling
In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets because their parameterizations do not specify…Feature Normalization under Finite Training Budgets
Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learning.Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models
Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data.Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization
We develop a comprehensive theoretical analysis of Matrix Shampoo for tracking time-varying optima in nonstationary stochastic optimization.ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty
Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional class probabilities unknown.CLASSO: a practical primal path-following algorithm for the constrained LASSO
We present CLASSO, a path-following method for the LASSO subject to general linear equality and inequality constraints.Measuring Variable Importance Using Proportional Inclusive Marginal Effects
Shapley-based measures are widely used for measuring variable importance in statistics and machine learning.