AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 48
Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models
Multivariate ordinal data along with covariates are commonly collected in problems ranging from alignment of language models with human preferences, as well as in recommender systems.FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning
Decision-focused learning (DFL) trains predictive models for downstream optimization, but existing methods largely assume centralized data.The Impact of Likelihood Tempering on the Limiting Predictive Moments of Variational Bayesian Linear Neural Networks
In wide Bayesian neural networks, Gaussian mean-field variational inference is prone to "prior dominance": the Kullback-Leibler (KL) regularization term of the ELBO outweighs the expected…Relative Wasserstein Spatial Depth for Cluster Number Selection in Distributional Data
Contemporary data in many scientific domains, such as images, media streams, biomedical omics, are naturally modeled as probability distributions in Wasserstein space instead of points in Euclidean…Lower Bounds for Parallel Diffusion Sampling
Standard diffusion samplers generate samples through repeated evaluations of a learned score function.Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines
Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that predictor using the average gradient outer…An Accuracy-Information Tradeoff for Loss-Difference Conditional Mutual Information
Loss-difference conditional mutual information (ld-CMI) uses the smallest of the standard observations in the supersample hierarchy of generalization bounds: it measures what a learner's loss…Sketched Calibration for Conformal Prediction under Covariate Shift
Weighted conformal prediction corrects for covariate shift by reweighting calibration scores with the likelihood ratio between target and source covariates.The optimal information complexity of VC learning
Steinke and Zakynthinou(2020) introduces the Conditional Mutual Information (CMI) framework of analyzing the information complexity of learning algorithms based on algorithm-dependent…Two-Sample Testing via Path-based Inference
Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also serve as objects of statistical inference.From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model
Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders.Retrieval-Based In-Context Learning: A Domain Adaptation Framework
In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based on similarity to the query, rather than sampled…Global error estimators for parametric monotone nonlinearities and neural approximations
We construct computable error estimators, which double as loss functions for neural networks, for a class of parametric nonlinear partial differential equations with a monotonicity property, and…Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions
High-dimensional Bayesian optimization (BO) often fits Gaussian process (GP) surrogates from far fewer observations than input dimensions.The Blind Spot Paradox: When Adaptive Classifiers Defeat Drift Detectors
Monitoring concept drift from an adaptive classifier's error stream creates an operational conflict with the model's own update loop.On the Sequentially Semiseparable Structure of SDE-Induced Kernels and Efficient Algorithms for Kernel-Based Estimation Problems
We study kernel functions induced by a linear time-invariant (LTI) stochastic differential equation (SDE), an approach that embeds prior knowledge about the function to be estimated directly into…Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates.Global Communication or Graph-Specific Memory?
Scalable Graph Transformers are commonly trained and evaluated on static large graphs in a transductive setup.DAG-CLIP: A DAG Learning Framework in the Presence of Latent Variables
Learning directed acyclic graphs (DAGs) to uncover causal mechanisms has attracted substantial attention in machine learning.Sampling Allocation of LinUCB: Optimal Design Limits in the Small-Gap Regime
We study the sampling allocation of LinUCB in the small-gap regime, where the reward gaps are of order at most $n^{-1/2}$ over the decision horizon $n$.