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 43
Unpaired Canonical Correlation Analysis
Canonical Correlation Analysis (CCA) is a fundamental method for multiview shared space learning.Certified by Abstention: Distribution-Free Guarantees for Chain-of-Thought Verifiers at Small Calibration Budgets
Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee.Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling
Conditional density estimation targets the full distribution of a response given covariates, as required, for example, for per-galaxy photometric redshifts.Cluster-Robust Prediction-Powered Inference
Data collection is often costly or logistically demanding, limiting both the questions researchers can pursue and how precisely they can answer them.Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets
We study the accuracy of Gauss-Newton curvature in ridge-regularized nonlinear least squares.The Silhouette Operator: Identifiability of Low-Rank Measures from One-Dimensional Projections
Structured recovery phenomena, such as restricted isometry properties in compressed sensing, have shown that high-dimensional objects can often be reconstructed from remarkably low-dimensional…Unbounded Characteristic and Universal Kernels
Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications.EntroPrefill: Renyi-Guided Context Pruning with Conditional Stability Guarantees for Retrieval-Augmented Generation
Mid-prefill pruning can reduce the sequence processed by deeper transformer layers, but attention concentration alone does not certify that discarded context is dispensable.Leaner Transformers Can Easily Learn to Cluster
Transformers have in-context learning capabilities, where some known learning algorithms can be executed in the forward pass through the model.Fluctuations of Nonlinear Observables in Mean Field Neural Network Training
Mean field limits describe the training dynamics of wide neural networks through the evolution of the empirical distribution of their parameters.AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
Causal discovery becomes particularly challenging when the available sample size is small relative to the number of variables.Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
Bayesian neural networks need not be fully stochastic to be universal conditional density approximators, but it remains open which parameters should be stochastic.Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data
Human learning is a dissipative dynamical process: mastery accumulates through practice, decays through forgetting, and propagates across interdependent concepts.Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields.Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking
Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers.Expected Sample Complexity in Multi-Armed Bandits
Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction between the agent and an environment.Possibilistic Radial Transport for Approximate IM Inference
Probing the hypothesis space after seeing the data remains valid under possibilistic inferential models (IMs), provided the significance level stays fixed.Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false negative rate remains…Controlling Dependence in Implicit Generative Models via Spread Mutual Information
Mutual information (MI) provides an objective for suppressing or encouraging statistical dependence in implicit generative models.Gaussian Equivalence for Multi-Head Self-Attention
A theoretical understanding of multi-head self-attention is fundamental to the study of modern neural networks.