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 10
Proving and Improving the Platonic Representation Hypothesis with JEPAs
Whether models trained on different modalities converge to the same representation of the world is central to AI.Satisficing Regret Minimization in Bandits with Knapsacks
We study satisficing regret minimization in bandits with knapsacks (BwK), where a learner sequentially selects randomized policies, i.e., distributions over arms, to obtain mean rewards above a…Safe Multi-Objective Reinforcement Learning: Zero Constraint Violation and Primal-Averaging Convergence
Many applications of multi-objective reinforcement learning (MORL) require policies that balance conflicting objectives while satisfying safety constraints.Conic-Lifting for Learning-rate-free Convex Optimization
A central topic in online and stochastic optimization is to design algorithms that do not require tuning of hyperparameters.The Cost of Working Memory in Streaming Mean Estimation
A streaming estimator must compute from the evidence that fits in memory.Non-Asymptotic Confidence Regions for Separable Nonlinear Models
This paper studies robust uncertainty quantification for separable nonlinear least squares (SNLLS) problems.TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals.Emergence of the transformer architecture as the score of entropic laws of random measures
We show that the transformer architecture emerges as the score of last-token laws in a natural statistical model of language, where sentences are empirical measures of tokens, a language is a law on…Formalizing the Intrinsic Dimension of Objective Landscapes by a Generalized Notion of Co-dimension
In this work, we formalize the concept of intrinsic dimension of objective landscapes introduced in Li et al. (2018), which measures the structural complexity of learning problems by training along…Bayes-Consistent Losses for Adaptive Inference in Classification
Adaptive inference systems balance accuracy and inference cost by routing inputs to models with varying capabilities.Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems
Reconfigurable Intelligent Surfaces (RIS) are a promising solution for improving millimeter-wave (mmWave) communications by controlling signal propagation.Probabilistic Meshless Methods on Manifolds
Probabilistic meshless methods solve partial differential equations (PDEs) by placing a Gaussian process prior on the solution and conditioning it on the PDE at a finite set of collocation nodes.Reinforcement Learning Under Runtime Confounding: Policy Learning and Off-Policy Evaluation
Reinforcement learning (RL) is increasingly used to train robust, adaptable systems across healthcare, e-commerce, and robotics.Fisher Contrastive Learning to Mitigate Feature Suppression: A Case Study in Acute Promyelocytic Leukemia Diagnosis
Self-supervised contrastive learning (SSCL) learns representations by contrasting augmented views of the same sample, but it can exhibit feature suppression, where dominant or easy-to-learn factors…Closing the Log Gap: Minimax-Optimal Regret for Episodic RL
In this paper, we study the regret minimization problem in episodic tabular MDPs.Hidden Disparities in Ranking: Category-Aware Fairness via Distributional Stability
We study the problem of fair ranking in recommender systems.Understanding axial attention in TSFMs through single location regression
Recent advances in Time Series Foundation Models (TSFMs) increasingly rely on axial attention mechanisms, but despite their empirical success, the theoretical properties of axial attention remain…Conformal Annotation with Randomized Expert Review: Application to Online Cell-Type Labeling
Artificial intelligence (AI) models can accelerate cell-type annotation in digital pathology, but safely scaling their use requires determining, with error control, which model-generated labels can…Statistical Analysis of Synthetic Data with Verification: A Linear Regression Framework
Synthetic data are widely used for training modern AI systems.Optimal Neural Estimation for High-Dimensional Nonparametric Regression under Multisource Covariate Shift
We study high-dimensional nonparametric regression under multisource covariate shift, where $\mathrm J$ sources of training data share a common regression function but differ in their covariate…