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 38
Predicting how model propensities generalize out of distribution
How do language models generalize from their training data?Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning
The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying Markov chain is mixing.Characterizing the Separability Gap in Prior-Data Fitted Networks
Prior-data fitted networks (PFNs) are transformers pretrained on simulated datasets to approximate Bayesian posterior predictive distributions.Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts
Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern large-scale systems.Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds
Data and the governing partial differential equations (PDEs) of continuous-time dynamical systems provide rich structure for statistical learning.Data-Topology Compatibility Enables Personalization in Decentralized Learning
Decentralized learning must balance collaboration with personalization across heterogeneous agents.Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions
Determinantal point processes (DPPs) on a finite set are probability models that exhibit repulsion where inter-item similarities are encoded by a positive definite kernel matrix.Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes
Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows.Universal Bounds for Self-Normalized Kullback-Leibler Estimation
This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights.Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels
Determinantal point processes (DPPs) model subset selection probability with repulsion, but many applications also exhibit attraction arising from pairwise relations or shared latent structures.Beyond Second-Order Information: Paid Logistic Regression with Noisy Features
We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underlying features.Sparse Dictionary Learning for Measure-Valued Data via Linearized Optimal Transport: Efficient Algorithms and Sample Complexity
We study dictionary learning for probability measure-valued data by embedding measures into Hilbert spaces using features derived from optimal transport geometry.From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance
Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differences involving the smaller sample.Spatial Prediction with Guided Diffusion
Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data.Scan-Parallel Variational Inference for event-driven Latent Jump Models
Continuous-time latent state-space models are widely used for irregularly observed time series, typically representing uncertainty through a random initial state followed by smooth ordinary…Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction
If a second text corpus were collected from the same population, how much would a predictor's outputs change?Target-Constrained Pooling for Trajectory Extrapolation
Trajectory extrapolation from a partial target trajectory is underdetermined, but completed related trajectories provide information about the unresolved continuation.Why Heavy-Tailed Weight Spectra Predict Model Quality
A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive.Fast Gradient-Based Approximation of Continuous Dynamic Time Warping
Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series.BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning
Kernel Ridge Regression (KRR) is a theoretically sound algorithm for nonparametric learning, which is also often used to investigate properties of deep learning.