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 6
Graph-Localized Coverage for Conformal Prediction on Inductive Graphs
Conformal prediction (CP) provides marginal coverage guarantees, but marginal coverage does not determine where prediction errors occur on a graph.Variational learning of energy-stable macroscopic flow equations from microscopic laws
We present a generalized variational approach for constructing energy-stable macroscopic partial differential equations (PDEs) directly from microscopic physical laws.Transport for Conditional Sampling: Foundations and Applications
Conditional sampling is central to Bayesian inference, generative modeling, and causal inference: given a joint law for random variables $X$ and $Y$ and an observation $Y=y$, one seeks to sample…Correctness and Predictive Uncertainty in Bayesian TSK Fuzzy Regression: A Reproducible Evaluation on Tabular and Building-Meter Data
Uncertainty estimates from fuzzy regression are informative only when the implementation is correct and the comparison protocol makes the sources of variation visible.Abstention Spends Its Budget on Local Label Variance More Than on Novelty: Auditing Conformal Calibration and Selective Prediction under Molecular Scaffold Shift
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property prediction: abstain on the least reliable compounds, ideally out-of-distribution chemistry, and…Principled Human-AI Collaboration in Agentic Workflows
Organizations adopting AI face the challenge of deciding which outputs can be released without human review while meeting the quality standards of human experts.When Does Sparse Routing Help? The Geometry of Expert Separation in Mixture of Experts
Sparse mixtures of experts combine multiple prediction models while evaluating only a small subset for each input.Surrogate Losses for Fairness-Aware Classification: Fisher Consistency and Calibration
Fairness-aware classification seeks to balance predictive accuracy and fairness across demographic groups.Sharp Generalization for Nonparametric Regression in Interpolation Space by Shallow Over-Parameterized Neural Networks with Channel Attention
We study nonparametric regression using an over-parameterized two-layer ReLU neural network with fixed covariance-based channel attention, trained by gradient descent with both layers updated…Near-Optimal $\sqrt{K}$-Regret for Online Learning in Uninformed Markov Games
We study online learning in two-player uninformed Markov games, where the opponent may follow an arbitrary, unknown policy in each episode and the learner does not observe the opponent's actions…Beyond Imputation: Mask-Adaptive Conformal Prediction via Tree Embeddings on General Missing Data Mechanisms
Conformal prediction (CP) provides a distribution-free framework for uncertainty quantification with finite-sample marginal coverage guarantees.How Does Chain-of-Thought Supervision Teach Models to Reason? A Theoretical Analysis
Training with chain-of-thought (CoT) supervision is an effective approach for teaching models multi-step reasoning procedures.Convex Regression at Scale: Geometry-Preserving Constraint Sparsification
Convex regression provides a flexible nonparametric estimation framework while incorporating appropriate shape information.Federated Estimation of Loading Spaces in Tensor Factor Models under Differential Privacy
Tensor factor models provide low-dimensional representations of tensor-valued observations while preserving their multiway structure.Watch Your Step: Adam's Margin Maximization Bias Depends on the Learning Rate Schedule
We study the implicit bias of Adam and its similarity to sign gradient descent on homogeneous models in binary classification tasks.Direct Knowledge Transfer for Offline Q-Learning
Data-driven sequential decision making in marketing and healthcare can benefit from observations collected in related environments, particularly when data in the environment of interest are limited.Rare Event Constrained Reinforcement Learning with Large Deviations
We study finite-horizon reinforcement learning with a safety constraint on the probability of reaching a failure set.Large Geometry Models Are Priors For Fast Physics Pretraining
Language and vision models became data-efficient by pretraining on plentiful unlabelled data; physical field models have such data, over ten million 3D shapes, but geometry pretraining designs its…Reduced Cost Influence Functions for Predict-then-Optimize under Noisy Data
Machine learning models deployed in decision-making pipelines, from vehicle routing to dynamic pricing to clinical decision support, must remain reliable in the face of low-quality training data.Optimal spectral shrinkage via Krylov approximation
Spectral shrinkage encompasses a broad family of estimators for high-dimensional linear regression that regularize estimation and prediction by applying spectral filters to the sample covariance…