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 44
Transition Path Sampling Using Koopman Operators and Exit-Time Optimal Control
Sampling transitions between metastable states is a central problem in dynamical systems theory and molecular dynamics in particular.Towards Calibrated Probabilistic Forecasts for Events of Interest via Outcome-Conditional Recalibration
Calibration is an essential requirement for probabilistic predictions to be useful for decision making.m-Set Adversarial Bandits with Winner Feedback
We show upper and lower bounds on the regret of $m$-set adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models (winner index, winner reward, sum of…Conformal Prediction for Spatially Dependent Data via Sequential Whitening
Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be.Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization
Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization.Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors
Split Gibbs sampling (SGS) is a popular framework for posterior sampling in Bayesian imaging inverse problems.Universal Local Error and Realized Amplification for the First-Order EDM Predictor
We analyze the first-order deterministic diffusion sampler of Karras et al. (2022), termed EDM, in 2-Wasserstein distance by separating two sources of error: local discretization error and its…Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation
Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process that follows a continuous-time Markov chain (CTMC).Computations of the slice genus and the unknotting number of links via machine learning
Links are disjoint unions of circles smoothly embedded in $S^3$.RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes.Finite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows
Wasserstein gradient flow extends gradient descent to probability measures.Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme
We propose a neural algorithm for sampling from distributions specified by unnormalized Boltzmann densities.Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss
What can a distribution-matching regularizer such as SIGReg in LeJEPA certify about contrastive learning?Revisiting Explainable AI through Model-Independent Concept Dictionaries
Modern applications of AI rely on increasingly complex models.Data Reuse in Non-Stationary Learning
We consider online learning in non-stationary environments, where the goal is to track an unknown parameter that switches abruptly between a finite set of recurring values.Dataset Pruning from First Principles: A Label-Free Linear Programming Approach
Dataset pruning reduces a large training set to a representative subset while preserving model performance.Koopman Observers for Diffusion Acceleration: Correcting Feature Forecasts with Shallow Measurements
Feature caching accelerates diffusion sampling by replacing expensive network evaluations with predictions from previously computed activations.Safe Meta-Policy Design with Risk Control
Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one.Training Parallel Speculative Draft Models by Directly Minimizing Expected Decoding Rounds
Speculative decoding accelerates large language model inference by using a low-cost draft model to propose tokens that the full-size target model verifies in parallel.Derivative Gaussian Processes on a Two-Direction Budget
Gradient observations promise more accurate Gaussian process (GP) surrogates, but the cost of incorporating them has long stood in the way of realizing that promise.