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.
Get the few papers worth your time in Machine Learning (Statistics) every morning.
Follow Machine Learning (Statistics) and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 72
Error Bounds for Statistical Estimators in BTL Model with Parametric Multivariate Utility Functions
We study preference elicitation under the Bradley-Terry-Luce (BTL) model where the true partworth vector is unknown and has to be estimated as a parameter with elicited preference information.SuperPCA: subspace analysis and an efficient algorithm for high-dimensional PCA
Principal component analysis (PCA) is a fundamental tool to reduce the dimensionality of the data in many applications.A Practical Guide on Graphical Model Validation
This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling.Gap-Free Streaming PCA Beyond Rank-One Updates: Near-Optimal Rates and Applications to Differential Privacy
Streaming principal component analysis (PCA) seeks to recover a leading spectral subspace in a single pass over a data stream.On Basis Function Selection for Sparse Gaussian Process Regression
Sparse Gaussian processes achieve $O(N)$ inference by replacing the kernel with an appropriate expansion in a fixed basis $\{φ_j\}$ on the input space.Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification
Commonly, classifiers and monitoring procedures are trained from labeled data by optimizing an objective such as the misclassification rate.Optimal Sequential Annotations for Off-Policy Evaluation
Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment.Automatic depth-based local center clustering via $β$-integrated local depth and adaptive grouping
Clustering is an unsupervised learning technique that partitions unlabeled data into groups.A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models.Gaussian-process surrogate indicators for residual-based adaptive GMsFEM
Residual-based adaptive GMsFEM for high-contrast elliptic problems repeatedly evaluates local weighted $H^{-1}$ indicators on every coarse neighborhood, making indicator evaluation a recurring cost…Additive Nonparametric Regression with Spatial and Network Objects
This article is motivated by an imaging application from the Adolescent Brain Cognitive Development (ABCD) study, aiming to predict task-based brain activation maps from t-fMRI using cortical…Rolling Conformal Prediction in Sequential Model Training
We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training.Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights
We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the learner recommends an action from it, and the…WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps
Reward fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples.The Like Trap: Multi-Stage Poisoning against Agents in Similarity-based Recommendation Systems
With recent advancements in large language models (LLMs) and LLM-based agents, these agents are becoming increasingly autonomous and gaining broader access to act on users' behalf on the internet.Certified Task-Conditioned Active Observability
Before acting upon an unobservable physical system, an autonomous agent must determine which latent distinctions govern downstream tasks, how many active interventions are necessary to certify them…Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection
Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows, and update rules.Stochastic Inertial Krasnosel'skii-Mann Iteration Achieves Near-Optimal Sample Complexity
We analyze a simple stochastic inertial Krasnosel'skii--Mann (iKM) method for finding a fixed point of a nonexpansive operator in a real Hilbert space.Exponential Family Synthetic Controls
We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets.Doubly robust target inference for generalized linear regression with completely missing covariates
Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses.