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 13
Is Your LLM Relay Serving What It Claims? A low-cost and high-power test for LLM Relay Auditing
Relay and reseller APIs have become a common way to access large language models (LLMs), yet users cannot directly verify whether they receive the models they request and pay for.Physics-Guided Flow Matching: A Generative AI Framework for Global Total Electron Content Forecasting
Accurate forecasting of global total electron content (TEC) is important for space weather monitoring and the reliable operation of satellite communication and navigation systems.Rènyi-Regularized Flow-Based Variational Inference for Bayesian Calibration
Variational inference with normalizing flows provides a flexible posterior approximation for Bayesian model calibration, but struggles when the posterior has several modes separated by regions of…Doubly Robust Deep Kernel Tests for Structured Outcomes
We propose a test of whether two interventions induce the same outcome distribution when outcomes are structured and high-dimensional, such as images, text and graphs.Low-degree polynomials lower bounds for clustering in small dimension
We study the computational limits of clustering $n$ observations drawn from a mixture of $K$ isotropic distributions in $\mathbb R^d$, in the regime where the number $K$ of groups is much larger…Cost-based variable selection for prediction models
Variable selection for clinical prediction models is commonly done on statistical grounds related to model fit.LinMix: Data Mixing as Linear Domain Attribution
Language model performance depends strongly on the mixture of domains in the pretraining data corpus.An end-to-end framework for uncertainty-aware validation of generative models
Density estimation with generative models is a common task in the physical sciences, for example in background estimation in particle physics.Graph Representation Learning via Adaptive Expectile Aggregation
Graph neural networks (GNNs) rely critically on neighborhood aggregation, yet conventional mean aggregation may fail to capture class-relevant distributional information, particularly in…An Input Skip Can Reparameterize a VAE Without Improving the Generator
An input skip can reduce reconstruction error while leaving a model's distribution of new samples unchanged.QD-PINN:A Physics-Informed Neural Network Approach to Bayesian Quickest Detection in High-Dimensional Diffusions
Quickest detection problems (QDPs) arise in a wide range of applications.Agentic Distributional Regression: Conditional Density Estimation through Agent-Written Programs
Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets.Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure
Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain.Anisotropic Trust Region Warping for Prior-Data Fitted Networks in Bayesian Optimisation
Prior-Data Fitted Networks (PFNs) replace the hyperparameter refitting of Gaussian Process (GP) surrogates in Bayesian optimisation with a single forward pass, at the cost of a prior that is fixed…Universal Short-Time Fourier Transform: Data-Adaptive Window-Length Selection for Time-Frequency Analysis
The short-time Fourier transform (STFT) characterizes the local frequency content of time-varying signals by evaluating the discrete Fourier transform over a sliding analysis window.Investigating the Early Signatures of Feature Learning in Deep Linear Networks
Deep neural networks learn data-dependent features, a capacity widely credited for their empirical success.An $Ω(T^{3/4})$ lower bound for projection-free online learning with linear minimization oracles
This work considers projection-free online learning algorithms with linear minimization oracles for handling the decision set.Finite-Sample Optimal Conformal Prediction Sets under Covariate Shift
We study conformal prediction under covariate shift with a known covariate density ratio, seeking prediction sets with minimum expected length under the target distribution.Finite-Step Geometry of Marginal-Preserving Velocity Fields in Generative Flows
Generative flows can admit different velocity fields that induce the same continuous marginal evolution but behave differently after numerical discretization.New Multiple Descent Phenomena Induced by Heterogeneous Data
Modern machine learning models are often trained on large datasets assembled from many different sources, whose statistical properties can differ substantially.