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 85
Flow Duality and Source Geometry for Categorical Generation
Continuous and discrete flow matching are usually treated as separate constructions.Learning Orthogonal Multi-Index Models Beyond Small Initialization: Incremental Learning, Competitive Dynamics and Symmetry
Recent work has identified incremental learning in shallow networks trained on single-index and multi-index models.Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
We continue the study of relatively smart learning, introduced by Dughmi and Pour (2026), which asks a supervised learner to compete, marginal by marginal, with every distribution-fixed error…DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications such as electroencephalogram (EEG) and…GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA).Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling
Slice sampling is a Markov chain Monte Carlo algorithm that draws its next state uniformly from a "slice"---a super-level set of the target density function---at each iteration, thereby providing…Speed Limit for Information Acquisition in Stochastic Learning Dynamics
Neural networks acquire internal representations through learning.Distribution-free inference on the number of changepoints
Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$.The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives
We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives.Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff
We study distributed one-dimensional mean estimation under a 1-bit communication constraint.Instability in Patient Clustering: A Multiverse Analysis of Unsupervised Clustering in the CENTER-TBI cohort
Understanding patient heterogeneity is key to improving prognostic modeling in traumatic brain injury (TBI).AlphaRJM: Reward-Jump Memory for Stochastic Return-Guided Alpha Discovery
Formulaic alpha discovery is a pool-dependent symbolic search problem in which informative feedback is observed primarily when a complete expression is evaluated.A Lower Bound for the Heavy-Ball Method on Smooth Convex Functions
Can the classical Heavy-Ball method, with arbitrary horizon-dependent parameters chosen in advance, achieve Nesterov's $O(T^{-2})$ last-iterate rate on every smooth convex objective?Optimal estimation for Functional Linear Regression with Noisy Discretized Data
In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are observed on a regular grid and contaminated by…Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity.High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Sparsity is a powerful structural resource in optimization and statistics.Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples…A Generalization of Amari's Bayesian Duality
Amari's contributions to information geometry and machine learning are well known.Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
An input may activate few hidden units even when different inputs collectively use an entire network.Critical initialization destabilizes higher input derivatives in wide scalar-input networks
The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on…