| 2026 | AAAI | A Differential Perspective on Distributional Reinforcement Learning. | Juan Sebastian Rojas, Chi-Guhn Lee |
| 2025 | ECAI | A Contrastive Diffusion-Based Network (CDNet) for Time Series Classification. | Yaoyu Zhang, Chi-Guhn Lee |
| 2023 | ICLR | Recursive Time Series Data Augmentation. | Amine Mohamed Aboussalah, Min-Jae Kwon, Raj G. Patel, Cheng Chi, Chi-Guhn Lee |
| 2022 | IJCAI | Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch. | Hyun-Rok Lee, Ram Ananth Sreenivasan, Yeonjeong Jeong, Jongseong Jang, Dongsub Shim, Chi-Guhn Lee |
| 2022 | IJCNN | Meta-free few-shot learning via representation learning with weight averaging. | Kuilin Chen, Chi-Guhn Lee |
| 2021 | ICLR | Incremental few-shot learning via vector quantization in deep embedded space. | Kuilin Chen, Chi-Guhn Lee |
| 2021 | IJCAI | Bayesian Experience Reuse for Learning from Multiple Demonstrators. | Mike Gimelfarb, Scott Sanner, Chi-Guhn Lee |
| 2021 | IROS | A Marginal Log-Likelihood Approach for the Estimation of Discount Factors of Multiple Experts in Inverse Reinforcement Learning. | Babatunde H. Giwa, Chi-Guhn Lee |
| 2021 | UAI | Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. | Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee |
| 2019 | UAI | Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. | Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee |