| Stronger and faster wasserstein adversarial attacks K Wu, A Wang, Y Yu International conference on machine learning, 10377-10387, 2020 | 35 | 2020 |
| Newton-type methods for minimax optimization G Zhang, K Wu, P Poupart, Y Yu arXiv preprint arXiv:2006.14592, 2020 | 30 | 2020 |
| Discovering many diverse solutions with Bayesian optimization N Maus, K Wu, D Eriksson, J Gardner arXiv preprint arXiv:2210.10953, 2022 | 25 | 2022 |
| 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, 44615-44657, 2023 | 24 | 2023 |
| 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 | 24 | 2022 |
| 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 | 15 | 2020 |
| The behavior and convergence of local bayesian optimization K Wu, K Kim, R Garnett, J Gardner Advances in neural information processing systems 36, 73497-73523, 2023 | 13 | 2023 |
| 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 | 7 | 2024 |
| 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 | 6 | 2023 |
| Understanding adversarial robustness: The trade-off between minimum and average margin K Wu, Y Yu arXiv preprint arXiv:1907.11780, 2019 | 4 | 2019 |
| Variational gaussian processes with decoupled conditionals X Zhu, K Wu, N Maus, J Gardner, D Bindel Advances in Neural Information Processing Systems 36, 46191-46211, 2023 | 2 | 2023 |
| Black-box variational inference converges K Kim, K Wu, J Oh, Y Ma, JR Gardner arXiv preprint arXiv:2305.15349, 2, 2023 | 2 | 2023 |
| Understanding stochastic natural gradient variational inference K Wu, JR Gardner arXiv preprint arXiv:2406.01870, 2024 | 1 | 2024 |
| Mixed Likelihood Variational Gaussian Processes K Wu, C Sanders, B Letham, P Guan arXiv preprint arXiv:2503.04138, 2025 | | 2025 |
| Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference J Wenger, K Wu, P Hennig, J Gardner, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems 37, 31316-31349, 2024 | | 2024 |
| Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference JP Cunningham, G Pleiss, JR Gardner, P Hennig, K Wu, J Wenger arXiv, 2024 | | 2024 |
| A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions K Wu, JR Gardner arXiv preprint arXiv:2407.10449, 2024 | | 2024 |
| Wasserstein Adversarial Robustness K Wu University of Waterloo, 2020 | | 2020 |
| A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions K Wu, JR Gardner NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty, 0 | | |