| 2024 | AISTATS | Large-Scale Gaussian Processes via Alternating Projection. | Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner |
| 2024 | ICML | Understanding Stochastic Natural Gradient Variational Inference. | Kaiwen Wu, Jacob R. Gardner |
| 2023 | ADMA | Resident-Based Store Recommendation Model for Community Commercial Planning. | Kaiwen Wu, Yanhu Li, Xiaofeng He |
| 2023 | AISTATS | Discovering Many Diverse Solutions with Bayesian Optimization. | Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner |
| 2023 | ICML | Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. | Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner |
| 2020 | AISTATS | On Minimax Optimality of GANs for Robust Mean Estimation. | Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu |
| 2020 | ICML | Stronger and Faster Wasserstein Adversarial Attacks. | Kaiwen Wu, Allen Houze Wang, Yaoliang Yu |
| 2019 | ICML | Distributional Reinforcement Learning for Efficient Exploration. | Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu |
| 2016 | ICNC | Research and practice of time-sharing device sharing mechanism in remote embedded experiment platform. | Kaiwen Wu, Yanmei Jin |