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 69
Boolean threshold functions, neuron capacity, and memory retrieval
How much information can a single neuron remember?Shrinking-Tube Concentration for Adaptive Markovian Stochastic Approximation
Adaptive algorithms increasingly make decisions while reshaping the dynamics that generate their future data.Robust Detection of LLM-Generated Text under Contamination
We study the detection of LLM-generated text under editing and contamination.Path-specific harm decomposition: A partial identification framework
A central goal when designing treatment policies is often to "do no harm", that is, to avoid interventions that improve average outcomes while worsening outcomes for some individuals.Nuclear Norm-Regularized Bayesian Matrix Completion
Matrix completion, the problem of estimating missing entries in a matrix from noisily observed ones, underlies a diverse array of problems such as recommender systems and counterfactual outcome…Intrinsic-Extrinsic Coupling in Learning Dynamics
A learner's current observations need not determine its response to further training.Anchored Extra-Proximal Methods: Optimal Higher-Order Methods for Monotone Inclusion Problems
We study the deterministic oracle complexity of finding approximate solutions to composite monotone inclusion problems, formed by the sum of a smooth single-valued monotone operator and a maximally…Riemannian Gradient Descent for Gaussian Mixture Models with unknown diagonal covariances
This paper investigates the numerical resolution of the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures.Low-Rank Friction for Memory-Efficient Transformer Pretraining
iKFAD is a recently proposed optimiser that replaces adaptive learning rates with adaptive friction in the momentum dynamics, yet performs as well as Adam.Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices
Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between computational efficiency and model fidelity.Strategic Self-Consistency
Self-consistency has become a popular technique for enhancing the reasoning abilities of large language models by generating multiple reasoning paths and selecting the final answer through a…Cost-Aware Best-LLM Identification using Dueling Feedback
Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the multi-armed…An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments.Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference
Optimizing fMRI scan duration and spatial resolution is critical for experimental design, yet traditional correlation-based approaches cannot quantify uncertainty or disentangle scanner measurement…Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold
We here develop a functional neural network, termed MatFAE, for learning trajectories on the Riemannian manifold of symmetric positive definite (SPD) matrices.Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal decision.Learning to Bias: Machine Learning-Enhanced Particle Filters
Sequential inference estimates latent states from noisy and incomplete observations.Ordinary Nonconvex SGD under Distance-Dependent Moments: Finite-Horizon Stationarity and Nagaev Bounds
Uniform noise-moment bounds exclude stochastic gradients whose variability increases with the iterate.Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding
Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, allowing a pretrained diffusion prior to be reused…High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied.