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 37
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation
Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain.Screening Collapse under Recursive Synthetic Data
Recursive synthetic-data training can degrade a fitted model, but its effects on the variable-screening stage that precedes model fitting are less understood.When do explicit penalties improve flow matching?
Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation.Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies
We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated parameterizations.Adaptive Robust Confidence Sets in High Dimensions
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown.Inference on the Value of Policy Improvement in Adaptive Experiments
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data.ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization
Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes.What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions
Sparse autoencoder (SAE) features are typically discovered from how their activations co-vary with behaviour, yet they are often used through interventions such as steering or ablation.Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering
We study eigenpair estimation for the tensorized, density-weighted Laplace--Beltrami operator from $n$ independent data points sampled from a multi-manifold mixture distribution where manifolds may…Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching
Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties.Thompson Sampling for Learning in Zero-Sum Games
Thompson sampling (TS) achieves logarithmic and asymptotically optimal regret in classical stochastic bandits, but its behavior in multi-agent learning remains less understood.Prior-Informed Generalizable Low-Rank Representation Learning via Regularized Adversarial Reweighting
Modern learning problems increasingly leverage data from heterogeneous sources to improve robustness and generalizability under distribution shift, often with high-dimensional parameters admitting…ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models
As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown.Copula Active Subspaces II: Error Decomposition, A Posteriori Estimation, and Sharpness of the Bounds
The Copula Active Subspaces (CAS) method of Part I estimates a non-Gaussian observation-noise density $π_{\boldsymbol{n}}$ from residual samples.Identication and Estimation of Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonpositive pairwise dependence.Strong Gaussian Approximation of Constant Learning Rate SGD: Bias and Trajectory Inference
This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD).Understanding Hidden Spatial Preferences in Bayesian Optimization
Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral.Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification
We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators.Prediction with Expert Advice under Heavy-Tailed Losses
We study prediction with expert advice over $T$ rounds with $K$ experts under heavy-tailed losses whose $(1+ε)$-moment is bounded by $θ>0$ for some $ε\in(0,1]$.Minimum Volume Optimal Transport for Multivariate Conformal Prediction
Optimal transport (OT) enables multivariate conformal prediction (CP) to construct flexible prediction sets that reflect the underlying complex distributional geometry while retaining…