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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Multi-Task Active Learning with Efficient Resource Allocation
Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment.Data Fusion for Errors-in-Variables
We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate…sHAIL-Causal: A Sequential Staircase Procedure for Invariant Causal Predictor Discovery
We introduce sHAIL-Causal, the causal specialization of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm: a sequential staircase procedure that ascends a nested hierarchy of…Learning Decision-Stump Thresholds in Context: Dynamics of Softmax Attention
Estimating a decision threshold requires locating observations near an unknown boundary.A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral
An inaccurate expert can still provide useful information after correction.Sample-Optimal Estimation of the Fréchet Inception Distance
The Fréchet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias [BSAG18, CF20].A theory of platonic representations in language models
Representations of translated sentences are similar in the inner layers of multilingual language models -- an observation connected to the platonic representation hypothesis, yet unexplained…Poisson empirical Bayes estimation of sums of random variables via minimum-distance methods
The estimation of sums of functions of observable and unobservable variables is a long-standing problem in statistics, with applications in many domains.How Inefficient Is Natural Gradient Descent? From Exact Optimality to Θ( \sqrt{ \log d } ) Divergence
Natural gradient descent (NGD) underlies common methods in ML.Conditional Flow Matching for Transport Between Markov Processes
Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories of Markov processes.Empirical-Bayes spectral partial pooling across related tasks
Spectral methods are central to high-dimensional statistics and machine learning, underlying procedures for covariance estimation, matrix denoising, representation learning, clustering, and latent…Assumption-lean logistic regression with missing covariates
Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully chosen imputation schemes for missing data, or…Redundancy and synergy in multivariate Gaussians via the Blackwell order
The goal of the partial information decomposition (PID) is to quantify the redundant and synergistic information that multiple sources provide about a target.HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning.DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes.A perspective note on likelihood approximation and inference for complex simulation models using a chain of aggregated normalizing flows
We present a new perspective on the problem of likelihood approximation within the framework of simulation-based inference that promotes scalable and controllable simulation routines for large-scale…Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds
Bayesian Optimization (BO) has become an established methodology for minimizing black-box functions of a vector input.Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
Accurate forecasting of pathological outcomes is a central problem in psychology.Structure, Not Belief: Correlated Thompson Sampling from LLM-Derived Covariance in Combinatorial Semi-Bandits
Combinatorial Thompson sampling (CTS) draws independent posterior samples for every arm, so its exploration dynamics ignore any relation among arms.The interface of data assimilation and machine learning
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system.