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 17
On the Error Analysis of Triangular Transport Maps for Conditional Generation via MMD-based Objectives
Conditional generative models parameterized by triangular transport maps are widely used for conditional sampling, yet theoretical guarantees on the quality of the conditional measures they produce…Projection-Based Federated Learning on Compact Submanifolds with Geometry-Independent Differential Privacy
Federated learning trains a shared model across clients without exchanging their data, yet the updates they transmit still leak information about individual records.How Geometry Shapes Generative Model Evaluation on Riemannian Manifolds
On Riemannian manifolds, generative-model evaluation is geometry-dependent since curvature and dimension jointly determine distances and volume growth.Hessian-Similar Optimization with Capped Participation: Lower Bounds and Virtual Masters
What is the round complexity of Hessian-similar optimization when each round can contact at most $s$ of $m$ clients?Approximate Universal Prediction under Interaction
Bayesian mixtures, and Solomonoff induction in particular, provide an idealized model of prediction learned by minimizing population log loss, and they underlie universal agents such as AIXI.When is early stopping beneficial under label noise? A phase diagram for kernel gradient flow
Early stopping is commonly used to prevent fitting label noise from degrading generalization.Rethinking Representation Geometry in Supervised Contrastive Learning under Long-Tailed Data
Supervised contrastive learning and its balanced variants have demonstrated their effectiveness for representation learning under long-tailed distributions.DBCM: Dual-Branch Calibration Module for Test-Time Adaptation in Time Series Forecasting
Non-stationarity induces test-time distribution shifts in time series forecasting (TSF), resulting in forecast residuals between predictions and the corresponding ground truth.Conditional Flow Models for Offline Maximum Entropy Reinforcement Learning
Offline maximum entropy reinforcement learning seeks to estimate the optimal soft action-value function \(Q^*\) from a fixed dataset without further interaction with the environment.Implicit Neural Representations for Variational Problems on Graphons
We propose a neural exploratory framework for variational problems over graphons, i.e., symmetric measurable functions $[0,1]^2 \to [0,1]$, that arise as limits of dense graph sequences.Covariance-Informed Ensemble Score Filtering: Embedding Localized Covariance Geometry into Score-Based Transport
error covariance models are central to atmospheric data assimilation because they spread partial observational information across space, variables, and dynamically coupled scales.Federated Nonparametric Regression with Heterogeneous Clients
Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty.Empirical concentration for chains with infinite memory
Concentration inequalities for time series usually involve unknown quantities measuring dependence.Optimal Multiclass Online Regret with Arbitrary Label Spaces
We determine the optimal regret in agnostic multiclass online learning with arbitrary, possibly infinite, label spaces.Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning
Long-term environmental IoT/LPWAN deployments often aggregate heterogeneous data streams from diverse devices without systematic auditing of each variable's actual coverage prior to analysis-an…Random Jump Forests
Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models.Exact Global Minimax Risk near Nonidentification
We study squared-error estimation when a nuisance parameter is unidentified on a submodel but affects a target that vanishes there.Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System
Hybrid forecasting systems combining machine learning and numerical weather prediction offer a promising route toward improved forecast skill while preserving physical consistency.Contextual Decision Making: Predictive or Generative
Contextual decision making uses observed covariates to guide decisions under uncertainty.Acquisition for Spherical-Linear Bayesian Optimization on the Cube
Recent work demonstrated that executing Bayesian Optimization (BO) using simply a linear surrogate with a clever transformation is enough to approximate the performance of the standard…