Duncan, Andrew

8 publications

NeurIPS 2024 Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew Duncan

COLT 2023 A High-Dimensional Convergence Theorem for U-Statistics with Applications to Kernel-Based Testing Kevin H. Huang, Xing Liu, Andrew Duncan, Axel Gandy

NeurIPS 2023 Energy Discrepancies: A Score-Independent Loss for Energy-Based Models Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian Vollmer, Andrew Duncan

AISTATS 2023 Energy-Based Models for Functional Data Using Path Measure Tilting Jen Ning Lim, Sebastian Vollmer, Lorenz Wolf, Andrew Duncan

JMLR 2023 On the Geometry of Stein Variational Gradient Descent Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch

AISTATS 2022 Grassmann Stein Variational Gradient Descent Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew Duncan

NeurIPSW 2022 Using Perturbation to Improve Goodness-of-Fit Tests Based on Kernelized Stein Discrepancy Xing Liu, Andrew Duncan, Axel Gandy

NeurIPS 2019 Minimum Stein Discrepancy Estimators Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, Lester Mackey