Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

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Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

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Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters

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Statistical field theory for Markov decision processes under uncertainty

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Bayesian Data Sketching for Varying Coefficient Regression Models

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Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets

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

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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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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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