Wu, Kaiwen

13 publications

NeurIPSW 2024 A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions Kaiwen Wu, Jacob R. Gardner

NeurIPS 2024 Computation-Aware Gaussian Processes: Model Selection and Linear-Time Inference Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham

AISTATS 2024 Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu, Jonathan Wenger, Haydn T Jones, Geoff Pleiss, Jacob Gardner

ICML 2024 Understanding Stochastic Natural Gradient Variational Inference Kaiwen Wu, Jacob R. Gardner

AISTATS 2023 Discovering Many Diverse Solutions with Bayesian Optimization Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

NeurIPS 2023 On the Convergence of Black-Box Variational Inference Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob Gardner

ICML 2023 Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

NeurIPS 2023 The Behavior and Convergence of Local Bayesian Optimization Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob Gardner

NeurIPS 2023 Variational Gaussian Processes with Decoupled Conditionals Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob Gardner, David Bindel

NeurIPS 2022 Local Bayesian Optimization via Maximizing Probability of Descent Quan Nguyen, Kaiwen Wu, Jacob Gardner, Roman Garnett

AISTATS 2020 On Minimax Optimality of GANs for Robust Mean Estimation Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu

ICML 2020 Stronger and Faster Wasserstein Adversarial Attacks Kaiwen Wu, Allen Wang, Yaoliang Yu

ICML 2019 Distributional Reinforcement Learning for Efficient Exploration Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu