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Kaiwen Wu
Kaiwen Wu
Verified email at seas.upenn.edu - Homepage
Title
Cited by
Cited by
Year
Stronger and faster wasserstein adversarial attacks
K Wu, A Wang, Y Yu
International conference on machine learning, 10377-10387, 2020
282020
Newton-type methods for minimax optimization
G Zhang, K Wu, P Poupart, Y Yu
arXiv preprint arXiv:2006.14592, 2020
272020
Local Bayesian optimization via maximizing probability of descent
Q Nguyen, K Wu, J Gardner, R Garnett
Advances in neural information processing systems 35, 13190-13202, 2022
132022
Discovering many diverse solutions with bayesian optimization
N Maus, K Wu, D Eriksson, J Gardner
arXiv preprint arXiv:2210.10953, 2022
122022
On the convergence of black-box variational inference
K Kim, J Oh, K Wu, Y Ma, J Gardner
Advances in Neural Information Processing Systems 36, 2024
92024
On minimax optimality of GANs for robust mean estimation
K Wu, GW Ding, R Huang, Y Yu
International Conference on Artificial Intelligence and Statistics, 4541-4551, 2020
82020
Practical and matching gradient variance bounds for black-box variational Bayesian inference
K Kim, K Wu, J Oh, JR Gardner
International Conference on Machine Learning, 16853-16876, 2023
62023
Large-scale gaussian processes via alternating projection
K Wu, J Wenger, HT Jones, G Pleiss, J Gardner
International Conference on Artificial Intelligence and Statistics, 2620-2628, 2024
52024
The Behavior and Convergence of Local Bayesian Optimization
K Wu, K Kim, R Garnett, J Gardner
Advances in Neural Information Processing Systems 36, 2024
52024
Understanding adversarial robustness: The trade-off between minimum and average margin
K Wu, Y Yu
arXiv preprint arXiv:1907.11780, 2019
52019
Black-box variational inference converges
K Kim, K Wu, J Oh, Y Ma, JR Gardner
arXiv preprint arXiv:2305.15349, 2023
12023
Understanding Stochastic Natural Gradient Variational Inference
K Wu, JR Gardner
arXiv preprint arXiv:2406.01870, 2024
2024
Variational Gaussian Processes with Decoupled Conditionals
X Zhu, K Wu, N Maus, J Gardner, D Bindel
Advances in Neural Information Processing Systems 36, 2024
2024
Wasserstein Adversarial Robustness
K Wu
University of Waterloo, 2020
2020
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