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 29
SurvICL: a Foundation Model for in-context Survival Analysis
Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression.Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits
We study how to evaluate $K$ target policies simultaneously in stochastic contextual bandits with linear reward structure.Upper bounds on the number of causal pieces in spiking neural networks
The input space of single-spike neural networks decomposes into causal pieces, and the number of causal pieces has been shown to provide a lower bound on the approximation error for some spiking…Source-Aware Dynamic Pricing with Strategic Buyers
In modern commerce, firms and platforms typically use customer features from different sources to personalize prices: some features are verified using seller records or third-party data, while…Learning smooth dynamics via Kinematic Interpolants
Inferring dynamics from real-world experimental data is inherently ambiguous, as many plausible dynamical systems can explain the same positional observations at measurement times.Computing Fréchet Means on Unknown Submanifolds of Riemannian Manifolds
Empirical averages are fundamental statistical primitives.Flat Loss, Evolving Features: Grokking in Modular Addition
Can a network keep learning while its training cross-entropy stays nearly flat at a positive level?Is a Linear Probe Evidence of a Linear Representation?
The linear representation hypothesis holds that pretrained vision models encode generative factors as linearly decodable directions in feature space, and comes with a standard methodology: train a…Optimal approximation of smooth functions by ReLU neural networks
We study uniform approximation of the Hölder ball $\mathcal H_1^s([0,1]^D)$ by fully connected ReLU networks.Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification
In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure outcomes for all available units.Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent
Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning.State-Matched Sequence Conformal Prediction
Sequential conformal prediction needs to decide which past errors should calibrate uncertainty for the current target.High-Probability Regret Bounds for Feel-Good Thompson Sampling
Feel-good Thompson sampling (FGTS) augments the posterior of vanilla Thompson sampling with an exploration term that favors reward functions with high predicted optimal values.Noisy-Label Calibration with Multiple Unreliable Labels
In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect.On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework
Differential Privacy (DP) provides a rigorous framework for quantifying privacy guarantees.Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation
We consider the problem of estimating the background intensity of a Hawkes process when the triggering mechanism is known, treating the background as an unknown finite positive measure with a…Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials
Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput bottleneck.On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization
Recent work has sought to transfer first-order nonconvex optimization guarantees to zeroth-order methods through gradient estimators, typically incurring a factor linear in $d$…Function-Space Temporal Self-Distillation for Policy Optimization
Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over response trajectories.Annealed Lévy Diffusion via Gaussian Surrogate Reverse Dynamics
Gaussian diffusion models offer a practical framework for generative modeling.