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 67
Maximum Likelihood Estimation for Entity Ranking under Iterative Synthetic Data Augmentation
We study entity ranking and inference under the Bradley--Terry--Luce (BTL) model using pairwise comparisons collected over sparse comparison graphs.A statistical theory for blind denoising: minimax estimation of the noise level from a single sample
Motivated by blind denoising in diffusion models, we study estimation of an unknown Gaussian noise level from a single high-dimensional observation, assuming the signal law P is known.Low-Rank Single-Index Bandits with Unknown Links: From Matrices to Tensors
Low-rank matrix and tensor bandits exploit structured interactions but typically assume a known reward link.MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors.Causal Retention in Interactive Agents: Interface Factorization and Selective Adaptation
Task performance need not determine which intervention mechanism an agent retains.Design-Ignoring versus Design-Respecting World Models for Epidemiology
World models for epidemiology learn from records shaped by study designs, including assignment, sampling, measurement, and related processes.Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence
Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of the normalization constant of model is…TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noise
We consider stochastic nonlinear optimization problems with deterministic equality constraints.Conformal Prediction under Exponential-Tilt Joint Shift
Conformal prediction can lose coverage when the data distribution changes after deployment.Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change
Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions.Ensembles of Exactly Solved Subsamples for Clusterwise Regression: Trimming Without a Trimming Level
Clusterwise least squares partitions regression data into K groups with separate linear fits.SAGE: A sampling-aware global evaluation benchmark for species distribution modeling
Knowing where species occur is fundamental for biodiversity research and conservation.A Flatness-Generalization Relation in the Teacher-Student Tree-Committee Machine
The flatness of the loss landscape at a minimizer is a widely used heuristic for reasoning about neural-network generalization, yet evidence for this relation is mostly empirical and controversial.DIAL: Position-Debiased LLM Judges with Adaptive Human Preference Calibration
Large language models (LLMs) as a judge enable scalable evaluation, but their judgments can be sensitive to response order and, even after removing such position effects, can still diverge…Geometric Moment Contraction for Stochastic Nesterov Acceleration
We study geometric moment contraction (GMC) of the constant-parameter stochastic Nesterov recursion \[ Y_k=Θ_k+β(Θ_k-Θ_{k-1}),\qquad Θ_{k+1}=Y_k-γG(Y_k,X_{k+1}). \] Under mean strong monotonicity…LUCID: Learning Under Confounding for Inference and Discovery in Time Series
Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct edges.Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by the difficulty of evaluating worst-case risks…Equation discovery with Bayesian tree-adjoining grammars
Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as a finite set of grammatical rules, from which…Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem.Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers
Transformers have become a central architecture for in-context learning (ICL), particularly through their state-of-the-art performance in large language models.