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 52
LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems
Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints without retraining.A multi-stage probabilistic framework to estimate gas-fired generator performance during extreme winter weather
Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quantify outage risk remain proprietary.Gaussian Flow Dynamics: Simulation-Free Neural SDE Learning Beyond One-Time Marginals
Simulation-free training of latent Stochastic Differential Equations (SDEs) relies on a variational posterior process whose one-time marginals are tractable, typically Gaussian.On the Trade-off Between Information Loss and Generalization in Sparse Attention
To mitigate the quadratic complexity bottleneck of the Transformer, sparse attention has emerged as a pivotal technology.Causally Fair Generation with Large Language Models
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their…Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery
External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs.Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking
We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model.Hypergraph Representation Learning with Hyperlink Random Effects
Hypergraphs record multi-way interactions among entities.Exact Fast Batch Simulation for Tabular Reinforcement Learning
Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajectories even when downstream procedures use only…Variance-Aware Fine-Grained Gap-Dependent Bounds for Online Reinforcement Learning
We study model-free online reinforcement learning (RL) for episodic tabular Markov decision processes, focusing on both gap-dependent regret and policy switching cost.When Is Enough Enough in Self-Evolving LLM Systems?
Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifacts.Calibration Assessment for Nested Uncertainty Sets
We present a Bayesian framework for assessing the calibration of nested uncertainty sets from simulation studies.Efficient Mass Matrix Estimation with Gaussian Cooling
We introduce an algorithm for efficiently preconditioning log-concave and log-smooth distributions that scales logarithmically with the condition number of the underlying distribution.Low-Rank and Structured Sparse Tensor Decomposition for Anomaly Detection in Multivariate Functional Data
Multivariate functional data arise in many modern manufacturing systems, where multiple sensors record densely sampled process trajectories.Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model
In this paper, we study the sample complexities of value and policy learning in finite discounted Markov decision processes (MDPs) under recursive entropic risk preferences with risk parameter…Generalization Bounds for Flow-matching Generative Models for Intrinsically Low-dimensional Data
Despite the remarkable empirical success of flow-matching models, their statistical generalization guarantees remain underdeveloped.Differential Privacy of Gradient Descent on Perturbed Objectives
Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer.Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit Feedback
We study fixed-confidence best-arm identification under strict 1-bit feedback constraints.Hold-Out Scoring for Efficient Gaussian DAG Learning
High-dimensional Gaussian DAG learning faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset search and a supplied indegree bound…Muon Learns Facts Better: Understanding the Role of Spectral Orthogonalization
The Muon optimizer applies spectral orthogonalization to matrix-valued updates and has shown strong performance in large-scale neural network training, yet the mechanisms of this transformation in…