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 15
Depth Improves Training-Time Scaling for Difficult Tasks in Deep Linear Networks
Neural scaling laws describe the predictable power-law dependence of model performance on training time, dataset size, and model scale, with the exponents quantifying how performance changes with…Balanced Sampling in Stochastic Multi-objective Bandits
In stochastic multi-objective bandits, Pareto optimal arms may represent different trade-offs among objectives.Provably Efficient Q-learning for Robust Reinforcement Learning
Standard reinforcement learning (RL) algorithms based on Markov Decision Processes (MDPs) are vulnerable to sim-to-real gaps.Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training, reasoning models, and robotic control.LUCID: Local Understanding of Covariate Importance with Conditional Density Estimation
Computing local-level variable importance (VI) is critical for understanding machine learning models.Model Selection for Off-Policy Evaluation via Pairwise Bellman Validation
Model selection and hyperparameter tuning pose a central challenge in off-policy evaluation (OPE) because errors of fitted Q-functions and occupancy-ratio estimates cannot be directly evaluated from…Recertifying decisions via e-closure: uniform improvements of conformal risk control and randomized e-BH
Statistical procedures often control expected loss at a prescribed level while leaving part of their risk budget unused.A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts
Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used.Anchored Stein Variational Gradient Descent Algorithm for Non-smooth Sampling
We introduce a Stein variational framework for sampling from a target density $p\propto e^{-U}$ when the target potential $U$ is possibly non-smooth.Learning on-the-fly quasi-stationarity diagnostics from low-dimensional surrogates
Accelerated dynamics methods, such as parallel replica dynamics, require estimates of the decorrelation time, after which a process trapped in one of its metastable states is distributed according…Minimax-Optimal Small-Loss Bounds for Online Linear Optimisation on $\ell_p$ Balls
We study full-information online linear optimisation on the unit $\ell_p$ ball in $\mathbb{R}^d$, for fixed $2\le p<\infty$ and losses of dual norm at most one.Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
Semiparametric Bayesian networks (SPBNs) are flexible probabilistic graphical models for representing joint distributions of continuous variables through lower-dimensional conditional probability…Symmetry Breaking Is the Price of Finite Capacity
If rotating a problem never changes its answer, one might expect its best model to be rotation-invariant; we show that this fails, predictably and by an exact amount, when capacity is limited.Anytime Valid Confidence Bands for Distribution Functions
Nonparametric inference often requires characterizing an unknown function, such as a cumulative distribution function (CDF), across its entire domain rather than a specific point.Approximation Families and the Statistical Efficiency of Denoising Score Matching
We study score matching over finite-dimensional approximation spaces generated by gradients of eigenfunctions of the infinitesimal generator of a forward diffusion.Directional Statistics on Circles and Spheres: A New Angle on Data
Two compass bearings near north can have an ordinary arithmetic mean pointing south: the difficulty is that angles live on a circle, not a straight line.Confidence Is Not a Promise: A Reference Audit of Typed Decision Models
Typed decision models return a probability distribution over options supplied by the caller, and software tends to act on that number as if it were a guarantee.A Nonconvex Framework for Joint Sparse Sample Weighting and Parameter Estimation
We propose a joint sparse sample weighting and parameter estimation (SWAPE) framework that jointly optimizes model parameters and sample weights to train a model while simultaneously assigning an…Constrained Gradient Equilibrium: Approachability under Locally Constrained Adversaries
Gradient equilibrium (GEQ) is a recent framework in online learning that requires the time-averaged gradients along the learner's trajectory to vanish.Partial Modal Tensor Regression
With the rapid development of modern scientific research, tensor data have become increasingly prevalent across a wide range of fields.