| 2024 | AISTATS | Model-based Policy Optimization under Approximate Bayesian Inference. | Chaoqi Wang, Yuxin Chen, Kevin Murphy |
| 2024 | AISTATS | Don't Be Pessimistic Too Early: Look K Steps Ahead! | Chaoqi Wang, Ziyu Ye, Kevin Murphy, Yuxin Chen |
| 2024 | ICLR | Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints. | Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, Yuxin Chen |
| 2023 | ICML | Active Policy Improvement from Multiple Black-box Oracles. | Xuefeng Liu, Takuma Yoneda, Chaoqi Wang, Matthew R. Walter, Yuxin Chen |
| 2021 | AISTATS | Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? | Chaoqi Wang, Shengyang Sun, Roger B. Grosse |
| 2020 | ICLR | Picking Winning Tickets Before Training by Preserving Gradient Flow. | Chaoqi Wang, Guodong Zhang, Roger B. Grosse |
| 2019 | ICLR | Three Mechanisms of Weight Decay Regularization. | Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse |
| 2019 | ICML | EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. | Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang |
| 2018 | ICML | Differentiable Compositional Kernel Learning for Gaussian Processes. | Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse |