| 2026 | EACL | Imbalanced Gradients in RL Post-Training of Multi-Task LLMs. | Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, Yonathan Efroni |
| 2025 | AISTATS | Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way. | Jeongyeol Kwon, Luke Dotson, Yudong Chen, Qiaomin Xie |
| 2025 | COLT | Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. | Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun |
| 2025 | ICML | A Classification View on Meta Learning Bandits. | Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar |
| 2024 | ICLR | On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. | Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak |
| 2024 | ICML | Prospective Side Information for Latent MDPs. | Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis |
| 2024 | ICML | On The Complexity of First-Order Methods in Stochastic Bilevel Optimization. | Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu |
| 2023 | ICML | Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. | Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li |
| 2023 | ICML | Reward-Mixing MDPs with Few Latent Contexts are Learnable. | Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2023 | ICML | A Fully First-Order Method for Stochastic Bilevel Optimization. | Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak |
| 2022 | ICML | Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. | Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2021 | AISTATS | On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. | Jeongyeol Kwon, Nhat Ho, Constantine Caramanis |
| 2020 | AISTATS | EM Converges for a Mixture of Many Linear Regressions. | Jeongyeol Kwon, Constantine Caramanis |
| 2020 | COLT | The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. | Jeongyeol Kwon, Constantine Caramanis |
| 2019 | COLT | Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. | Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis |