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 18
Score-Preserving Continual Lasso under Task Shift
Continual learning (CL) aims to learn from sequential tasks while mitigating catastrophic forgetting when historical data are no longer accessible.Strongly Adaptive Online Learning with Time-Varying Movement Costs
We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's performance over arbitrary sub-intervals.Sparse Bayesian Neural Networks via Sticky Piecewise Deterministic Markov Processes
There is growing evidence that the performance of a neural network depends largely on a small subset of its weights, which are typically identified by training a dense network and pruning it…Structure and Exact Certification of Nonnegative Tensor Decompositions
We study the structure and exact certification of nonnegative tensor decompositions supplied through rational factor matrices.Generalization properties of the token perceptron: the role of embedding and vocabulary
Large language models are trained to predict tokens from a finite vocabulary, each represented by a vector in a $d$-dimensional embedding space.Neural LoFi with Backward Coupling: A Spectral Theory of Cross-Layer Feature Learning
During joint training, backpropagation allows later layers to reshape earlier representations, a feedback mechanism absent from purely layer-wise learning.Mixed Engression: Distributional Regression for Multivariate Mixed-Type Responses
Estimating the conditional distribution of multivariate responses with continuous and binary components requires capturing their dependence, respecting their different supports, and accommodating…Singular Geometry of Low-Rank Adaptation
Low-rank adaptation (LoRA) is now a standard approach to parameter-efficient fine-tuning of large neural networks.A Measure-Geometric Theory of Robustness in Machine Learning
This paper develops a foundational measure-geometric theory of robustness in machine learning.Kernelised covariance matrices, dimension reduction and imbalanced spectral clustering
The Gaussian Mixture Model (abbrv.Not All Mislabels Are Equal: Efficient Task-Aware Auditing with Confidence Sequences
We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream…The kernel mean embedding of a random probability distribution and its principal components
Given a random probability distribution \(F\), whose realizations are defined on \(\mathbb R^d\), and a kernel \(k\), our aim is to analyze the properties of the kernel mean embedding, \(μ_F\), of…Blind Random Search with Noisy Measurements: Almost Sure Convergence and Convergence Rates
Blind random search is a simple global optimization method that only requires the value of the loss function, without needing gradient information or an explicit loss model.When Are Nesterov Trajectories Eventually Locally Minimizing?
We revisit the variational characterization of Nesterov's celebrated method and study when Nesterov trajectories are eventually locally minimizing in a variational sense, a notion that we define…Lifetime-valid decisions by continually evolving AI analysts
AI agents increasingly act as analysts whose decision can often be cast as hypothesis tests.Residual Calibration via Local Feature-Space Refinement
Deep neural networks often struggle to provide reliable confidence estimates, making post-hoc calibration essential for reliable decision-making.Width and Depth Limits Commute During Training in Linear Residual Networks
Recent work established that the infinite-width and infinite-depth limits of residual networks commute at initialization when residual branches are scaled by $1/\sqrt{depth}$.xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs
Graphs play a crucial role in many applications, ranging from drug discovery to social network modeling and astronomy.Continuous-depth Deep Gaussian Processes
Deep Gaussian processes (DGPs) are appealing Bayesian regression models, but their standard discrete-depth parameterization often becomes harder to optimize as depth grows.Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms
Random projections (RP) offer an effective method for reducing computational and storage overhead while preserving the geometric structure of data.