Papers
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki et al.
Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities
Rocco Caprio, Juan Kuntz, Samuel Power et al.
Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors
Gaoyu Wu, Jelena Bradic, Kean Ming Tan et al.
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito et al.
Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters
Florian Brück, Jean-David Fermanian, Aleksey Min
Statistical field theory for Markov decision processes under uncertainty
George Stamatescu
Bayesian Data Sketching for Varying Coefficient Regression Models
Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas
Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
Takeyuki Sasai, Hironori Fujisawa
Causal Effect of Functional Treatment
Ruoxu Tan, Wei Huang, Zheng Zhang et al.
Uplift Model Evaluation with Ordinal Dominance Graphs
Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke et al.
High-Dimensional L2-Boosting: Rate of Convergence
Ye Luo, Martin Spindler, Jannis Kueck
Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers
Federico Bassetti, Marco Gherardi, Alessandro Ingrosso et al.
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
Seunghoon Paik, Michael Celentano, Alden Green et al.
On Inference for the Support Vector Machine
Jakub Rybak, Heather Battey, Wen-Xin Zhou
Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis
Hongru Yang, Yingbin Liang, Xiaojie Guo et al.
Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
Atticus Geiger, Duligur Ibeling, Amir Zur et al.
Implicit vs Unfolded Graph Neural Networks
Yongyi Yang, Tang Liu, Yangkun Wang et al.
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
Brendon G. Anderson, Ziye Ma, Jingqi Li et al.
GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia
Carlo Lucibello, Aurora Rossi