AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 76
A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression
Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments.Next-token functional estimation
Suppose we observe the first $n$ points of a sequence of random variables having length $n+1$, and wish to estimate a functional of the unobserved final point and the empirical measure of the $n$…Compressed Active Subspaces for Scalable Bayesian Inference
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions that have the…Portfolio-Based Constrained Multi-Objective Bayesian Optimization for Materials Design
Materials discovery and design campaigns can be formulated as constrained multi-objective Bayesian optimization (CMOBO) problems, within which each experimental decision negotiates between two…Improving Sample Efficiency in Peptide-HLA Binding Prediction with Hybrid Quantum-Classical Neural Networks
Peptide-HLA binding prediction is a critical step in neoantigen identification for personalized cancer immunotherapy and holds significant clinical value.Learn Your Own Thoughts: Abstract Token Curriculum
Large Language Models (LLMs) have achieved remarkable reasoning capabilities by utilizing chain-of-thought (CoT) as a scratchpad for intermediate stages of thinking.Dual-Axis Policy Optimization for LLM Agents: Bayesian Feedback Attribution and Trajectory Mass Normalization
Reinforcement learning for LLM agents involves two distinct optimization di- mensions: how environment feedback is exploited within a trajectory, and how complete trajectories are aggregated across…Error bounds in Sobolev norms for approximations with norm constrained ReLU neural networks
Recent studies have shown that smooth functions can be well approximated by ReLU neural networks with path norm constraint on the weights.Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measures
In this paper, three operations on Gibbs probability measures are studied.Near-Optimal Pure Single-Loop Extragradient Method for Strongly Convex--Strongly Concave Minimax Optimization
We study smooth strongly convex--strongly concave minimax optimization with general nonlinear coupling in the deterministic unconstrained setting.Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning
Offline policy evaluation (OPE) is crucial in high-stakes reinforcement learning applications, where new policies must be assessed reliably before deployment.The Bias of Nonlinear Two-Time-scale Stochastic Approximation under Constant Step-Sizes
Two-timescale stochastic approximation (TTSA) is a fundamental tool for analyzing coupled iterative algorithms in reinforcement learning, optimization, and stochastic control.Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs
Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation.Resolution limits for process comparison from event data
One hospital runs bloods and imaging at the same time.Sharp spectral norm concentration of sparse random tensors
We prove a sharp concentration inequality for the spectral norm of sparse random tensors with independent Bernoulli entries.Parallelism, critical windows, and separations among diffusion language models
A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more efficiently than autoregressive models, which…TAP Accuracy Below the Fluctuation Scale and Universal Posterior Geometry in Spherical Linear Models
We study the Bayes-optimal spherical linear model as the ambient dimension and sample size grow proportionally, under a quantitative Marchenko--Pastur spectral-regularity condition on the design.Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions.Robust Multi-Task Learning for Principal Component Analysis
Principal component analysis (PCA) is a fundamental tool for learning low-dimensional structure from high-dimensional data.Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported laws may admit…