Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals

Subjects: “…Artificial Intelligence & Machine Learning…”
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Invariant Subspace Decomposition

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Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights

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Outlier Robust and Sparse Estimation of Linear Regression Coefficients

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Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares

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Causal Effect of Functional Treatment

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Uplift Model Evaluation with Ordinal Dominance Graphs

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High-Dimensional L2-Boosting: Rate of Convergence

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Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers

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How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences

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Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test

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On Inference for the Support Vector Machine

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Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

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Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

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Implicit vs Unfolded Graph Neural Networks

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Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification

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GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

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Dynamic angular synchronization under smoothness constraints

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Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

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Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds

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