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 33
Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension
We study learning an unknown element $g$ of a group acting with finite orbits on a countable set, from examples $(x,gx)$.Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence
Randomly initialised neural networks of width $m$ converge to Gaussian processes as $m \to \infty$, with two types of quantitative bounds characterising this convergence.Hierarchical Time Series Forecasting with Operator-Valued Kernels
Hierarchical time series forecasting requires generating accurate predictions that are coherent across multiple levels of aggregation.Operator Surrogates
This chapter provides a mathematical introduction to operator surrogates, which are finite-parametric maps approximating complex (typically nonlinear) operators between infinite-dimensional…On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning
Random features provide a standard approach to scaling kernel methods to large datasets by approximating the kernel with a finite-dimensional feature map.Principled Design of Expected Improvement for Continuous Simulation Optimization
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design choices…Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits
Many related prediction problems, which we call questions, come with few labels each.How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than $10$ parameters rather…Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too
Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models.Understanding Circulant Permutation to Extend the CMinHash Estimator
CMinHash promises a new direction for designing MinHash algorithms with reduced variance and storage space via circulant permutations.Minimax rates for pointwise CATE estimation: the role of design smoothness
We study the role of design smoothness in the minimax estimation of conditional average treatment effects at a fixed covariate point.BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling
Generative models are often required to serve many downstream conditional tasks.From Fourier Amplitudes to Persistence Images for Impact-Echo Defect Detection
Impact-echo testing is a nondestructive testing method used to detect internal defects in concrete structures.A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bounds for stochastic gradient…Designing for Prediction: Planning Formulas for Expected Out-of-Sample $R^2$ and $r$
Design calculations let researchers gauge, before data is collected, whether a planned study is likely to detect an effect.Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and…Delay Complexity Dimension: Learning with Delayed and Censored Labels
We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window $W$.Linear regression with probabilistic networks for network-linked data
Network-linked data combine a network with covariates and responses observed for its nodes.Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations
This paper studies Wong--Zakai convergence and Fourier neural operator learning for stochastic Volterra integral equations (SVIEs) describing random systems with memory.Semi-Amortized Bayesian Inference with Reusable Local Posteriors
Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable.