AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 81
Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reaction pathways are…Direct Conditional Transition Sampling for Diffusion Inverse Problems
Training-free diffusion inverse solvers typically choose between local measurement guidance and costly clean-space posterior updates.PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement
This study addresses the PU classification problem under violations of the SCAR assumption.From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift
Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty associated with their estimation.A Functional SVD Framework for Regularized Multivariate Functional PCA with Dual Penalization
This paper introduces a novel framework for Regularized Multivariate Functional Principal Component Analysis (ReMFPCA) via Functional Singular Value Decomposition (SVD).Curvature-Independent Regret Bounds for Distributed Online Optimization on Hadamard Manifolds
This work addresses decentralized online Riemannian optimization on Hadamard manifolds.Online Bayesian Node Classification on Inductive Graphs under Distribution Shift
On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and calibrated uncertainty for safety-sensitive…On the Equivalence of Stochastic Control and Path Space Formulations for Schrödinger Bridges over Compact Connected Lie Groups
We establish the equivalence between the stochastic optimal control and path space formulations of the Schrödinger bridge problem (SBP) for the kinematic equation on a compact connected Lie group.Resolution-Independent Analysis of Encoder--Decoder Operator Learning via Limiting Kernels
Operator learning is formulated on function spaces, but training data are typically available only through finite-dimensional representations.Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints
This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples.Linear Ensemble Sampling with Smaller Ensembles
Ensemble sampling offers a practical approach to randomized exploration by maintaining a collection of models, but how small an ensemble can be while retaining strong regret guarantees remains…Just add noise: Debiasing tree-based variable importance in mixed data
Variable importance scores from tree-based methods such as random forests favor continuous predictors over categorical ones.Exact Finite Attention Responses From RoPE Derivatives
We derive exact local responses for attention interventions, allowing candidate edits to be scored from a cached baseline and one backward pass.Riemannian ascent--descent for nonconvex nonconcave minimax landscapes: convergence to basin saddle points and applications to distributionally robust optimization
We study a class of distributionally robust optimization (DRO) problems for the statistical risk problem, formulated as minimax problems over the product of a Euclidean space and a Riemannian…On Large-Scale Multiple Testing Over Networks: A Non-Asymptotic Approach
Distributed multiple testing asks $N$ sites to control a global false discovery rate (FDR) under a tight communication budget.PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks
We study the problem of estimating unknown parameters in PDE-constrained inverse problems from noisy observations, where the PDE solution is approximated using Physics-Informed Neural Networks…Membership Inference via Pairwise Likelihood Ratios
Membership inference attacks (MIAs) are the standard tool for auditing the privacy risks of machine learning models.Representation-based Masked Diffusion Model
Masked Diffusion Models (MDMs) have emerged as a compelling paradigm for language modeling, offering the capability for efficient parallel text generation.A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data
Dementia is a major and growing global health burden, with Alzheimer's disease (AD) accounting for most cases.Inference for Newton Methods with Accelerated Sketch-and-Project via Random Scaling
We study an online sketched Newton method that approximates the Newton direction at each step via a state-of-the-art sketching solver, called the generalized accelerated sketch-and-project solver…