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 78
Fast Learning Rates for Physics-Informed Kernel Methods
In physics-informed machine learning, a target function $u^*$ is learned from noisy value observations $y_i=u^*(x_i)+ \varepsilon_i$, together with differential information, given either by noisy…A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings
Kernel methods, and Gaussian Processes (GPs) in particular, require a Hilbertian distance measure---one whose square is conditionally negative definite (CND)---to guarantee positive…Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation
Visual signals require compact yet sufficient representations for robust downstream prediction.Federated Soft Clustering via Generalized Total Variation Minimization
We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaussian mixture model (GMM).Not All Nodes Are Created Equal: Homophily-Aware Stratification for Stable GNN Evaluation
Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test splits.When a High Score Is an Illusion: Certifying Genuine versus Repackaged Forecasting Skill
Ranks depend on the observations used for comparison.Learning Submanifolds for Subsequent Inference on Random Dot Product Graphs, Part 1: Theory
We propose a framework for restricted inference on random dot product graphs whose latent positions lie on an unknown low-dimensional support manifold.Stable Policy Learning
In evidence-based policymaking, typically one experimental sample is observed, then a learned policy recommendation is implemented at scale.Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics
Antimicrobial resistance is expected to claim 10 million lives per year by 2050, and resource-limited regions are most affected.Null importance: Disentangling relevance for interpretable machine learning
Feature importance is central to interpretable machine learning, but the term "importance" encompasses several fundamentally different notions of relevance.Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
This tutorial provides a unified view of the emerging area of bilinear learning and control.Certified Inference and Training for Deep Equilibrium Networks: A Continuation Framework with Polynomial Complexity Guarantees
We develop a certified continuation framework for inference and training in deep equilibrium networks (DEQs), with training posed as interpolation to accuracy $2^{-b}$.Causal Path Analysis from Perturbational and Population-Scale Single-Cell Data with Multiscale Confounding and Measurement Error
Single-cell perturbation experiments provide causal information on gene regulation, whereas population-scale single-cell studies characterize gene expression and phenotypes in human populations.Supervising the Chain Ladder
The chain ladder's volume-weighted pattern minimises an explicit loss function, yet is rarely booked as such.Stable by Construction: Variational Latent Markov Operators for Long-Horizon PDE Prediction
Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can induce distribution…Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transport
Parameter estimation in finite mixture models can exhibit highly heterogeneous convergence behavior: locally isolated components may be estimated substantially faster than groups of competing…Time-warping estimation via stationarity-based learning of the de-warped signal
Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis.On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Weighted majority votes are central to many successful ensemble methods.HyCoSeq: Contextual Hyperbolic Representation Learning for Genomic Sequences
Hyperbolic geometry provides a natural inductive bias for genomic representation learning, but existing hyperbolic genomic models primarily use Lorentz convolutions to learn local sequence…Causal Discovery via Transformed Low-Rank Quantile Surfaces
We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank…