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 8
HALO: Hierarchical Evidence Selection and Reinforcement Learning for Allocating Budgeted Label Optimization from Longitudinal Clinical Records
Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and heterogeneous documentation--under a finite labeling budget requires solving two coupled…Self-Normalized Inference from Riemannian Stochastic Gradient Paths
Riemannian stochastic gradient paths estimate constrained parameters, but confidence regions usually require estimating the Hessian and gradient-noise covariance.Minimum-Norm Interpolation in Weighted Variation Spaces: Direct and Inverse Problems
We study minimum-norm interpolation in weighted variation spaces generated by ReLU$^s$ ridge functions on the unit ball, where $s$ is a positive integer.Selective inference for pruned interaction trees with known unequal variances in randomized trials
Interaction trees reuse outcomes to discover treatment-effect heterogeneity, complicating inference after growth and pruning.Lower Bounds on Inference Complexity for Polyak-Ruppert Averaged Stochastic Gradient Descent
In this paper, we establish sharp lower bounds for Gaussian approximation of Polyak-Ruppert averaged stochastic gradient descent with polynomially decreasing step sizes.Adaptive Optimizers Can Get a Policy Ranking Wrong: Momentum, Dependent Data, and a Two-Run Remedy
Operators choosing among decision rules, such as when a battery charges or where an idle taxi goes, rank them by estimated long-run value.Accelerating posterior sampling in Bayesian inverse problems with diffusion models: an online-offline perspective
Bayesian inverse problems require both informative prior models and computationally feasible posterior inference.Infinite Quantiles Inference under Local Differential Privacy
We study simultaneous quantile inference under local differential privacy for a number of target probabilities that grows with the sample size.MUON-RT Reusing the Teacher's Trajectory for Momentum-Free Muon for LLM Self-Distillation
Self-distillation methods for large language models with an exponential moving average (EMA) teacher already retain a history of student updates.Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks
Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs.A Unified Framework for Probability Estimation of Data-Dependent Events
We study the general problem of estimating $D(\dde(S))$, the probability that an unknown distribution $D$ assigns to an event determined by a sample $S$ of $n$ i.i.d.\ draws from $D$ through a known…Stochastic Mirror Descent under Dependent Observations
We study the non-asymptotic convergence of Stochastic Mirror Descent (SMD) algorithm when the noisy first-order oracle is driven by dependent observations.Instrumental Transfer Learning for Solution Operators
Data-driven learning for the solution operator of a differential equation has broad scientific importance, but observations from a target system are often scarce and costly to collect.Coarse-Graining Optimal Transport for Rollout-Free Post-Training of Flow Matching
Flow matching depends critically on the coupling used to pair source and target samples.Conditional Neural SDEs for Financial Time-Series Generation
Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external…Reliability-Weighted Direct Preference Optimization: Penalized Weights for Robust Preference Learning
Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons.PDE-Residual-Informed Kriging
Physics-informed Gaussian process (GP) methods provide a probabilistic framework for PDE solving.Bethe Hessian Recovery through the Kesten-Stigum Regime
We show that outlier eigenvectors of the Bethe Hessian achieve weak recovery in the stochastic block model down to the Kesten-Stigum threshold, for every fixed common expected degree $d>1$.Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective
Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty.Chronos-2: A foundation model for multivariate time series forecasting
Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences.