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 40
Markov Chain Convergence in Kernel-Induced Geometry
We study the convergence of Markov chains in the geometry induced by Hilbertian embeddings associated with positive definite kernels, also called Maximum Mean Discrepancy (MMD).A Stochastic Analysis Perspective on Diffusion Models
Generative modelling involves two complementary tasks: learning a probabilistic approximation of an unknown data distribution from observations, and designing a tractable mechanism for sampling from…Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters
Variance-based methods recover a Gaussian linear structural equation model (SEM) with equal or suitably constrained error variances, either by recursively identifying the source nodes with smallest…Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design
Augmentation-based self-supervised learning can recover the content shared across views without separating its underlying factors into individual coordinates.De-biased Distribution Estimation for Locally Differential Private Survival Data
Sharing time-to-event data can leak sensitive information about individual participants in clinical studies.Decomposing A Neural Learner
Neural learners for classification and regression are often criticized as black boxes.Decision-Sufficient Posterior Approximation
We investigate the consequences of requiring a posterior approximation to preserve a specified downstream decision problem.A General $\widetildeΩ(\sqrt{T γ_T})$ Lower Bound for Kernel Bandits
The kernel bandit problem consists of sequentially optimizing an unknown function with noisy feedback, where the function has bounded norm in a given Reproducing Kernel Hilbert Space (RKHS).Accelerating Non-Smooth and Heavy-Tailed Sampling
Anchored Langevin dynamics (ALD) is useful for non-smooth sampling where the density of the target distribution is possibly non-differentiable and heavy-tailed; reflected anchored Langevin dynamics…Tight Bounds for Equivalence Testing with Non-Adaptive Conditional Samples
We study distribution testing with access to non-adaptive conditional samples.When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization
Weight-only post-training quantization (PTQ) relies heavily on reconstruction loss minimization to preserve model quality at low precision.Regularized Small Area Estimation with Graph Laplacian Benchmarking priors
Small area estimation (SAE) often requires both borrowing information across areas and benchmarking estimates to reliable aggregates.From Geometry to Generalization: Why Row Normalization Can Beat Adam and Muon
Different optimizers can fit the same training data while selecting classifiers with substantially different geometries, but whether this difference provably affects population performance remains…Sequential Conditional Independence Testing with Machine Learning Models
Conditional independence testing is a ubiquitous problem in scientific discovery.Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data
Synthetic tabular generators are commonly optimized for distributional fidelity, but statistical similarity alone does not guarantee preservation of causal effects.Feature Space Adaptation for Effortless Gaussian Process Flows
Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging.Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD
Population gradient flow is a common tool for reasoning about how neural networks adapt, including after pretraining.PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics
Sparse identification of nonlinear dynamics (SINDy) is a data-driven framework for discovering governing dynamics from time-series data by identifying a sparse subset of candidate dynamical terms…LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression
Deep randomized models fix hidden-layer parameters through random initialization and learn only closed-form readouts, typically adding depth by stacking random trans formations without target-aware…Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks
Spatio-temporal tracking data has opened new possibilities for detecting complex tactical patterns in football, yet modeling the interactive movements of multiple players remains challenging.