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 87
Recovering Weak Signals with Normalizing Flows
In many scientific disciplines, weak signals of interest are obscured by dominant nuisance signals that are several orders of magnitude stronger.Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling
Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computational constraints demand compact yet faithful…Stability and Generalization of Straight-Through Estimators for Training Two-Layer Quantized Neural Networks
We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Statistical Learning Theory (SLT).Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality
The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning.RoPE attention is an exact forward-pass gradient step with softmax intact
We derive an exact gradient-step representation of the RoPE-softmax forward pass.Cluster-Based Dimensionality Reduction by Nonparametric Distributional Screening
We consider dimensionality reduction for high-dimensional observations accompanied by a supplied partition into two or more clusters.Large Classification-Risk-Optional Label Acquisition
We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk.A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation
The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symmetric matrix and its perturbation.Diagonal Attenuation: A Finite-Sample Correction for PCA
Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data.Online Learning with LLM Experts from Limited Feedback
We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback.Functional Attentive Interpretable Regression
In function-on-function regression, the coefficient surface $β(s,t)$ may exhibit complex support structure---from localized patches to global patterns such as disconnected regions, bands, or…Selective Posterior Margin Regularization for Forward-Corrected Classification
Learning with class-conditional label noise often relies on a transition model from latent clean classes to observed annotations.On-the-go Forgetting without Explicit Unlearning via ERASE
Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability.The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization
Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical studies mainly rely on the bounded variance…Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models
Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited.Recovering linear images of sparse signals from indirect observations
In this paper, we develop and analyze techniques for recovering a linear image $Bx$ of an unknown signal $x$ from indirect noisy observation $ω=Ax+ξ$.Robust conditional dimension reduction for dissimilarity data
Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data.An Analysis of Self-supervised Pre-training with Dependent Samples
Self-supervised learning relies on so-called data augmentations $φ(x)$ of unlabeled datapoints $x$ --- for example, masking random pixels in an image $x$ --- that should leave the label of $x$…Faster Learning under Relaxed Local Differential Privacy
We consider density estimation under the relaxed local differential privacy condition that the privatized distributions are $α$-close in total variation distance.Reconciling Universal and Uniform Learning with $Q$-Aggregation
We study regression under bounded responses in terms of excess mean squared error.