A Fully First-Order Method for Stochastic Bilevel Optimization
J. Kwon, D. Kwon, S. Wright and R. NowakProceedings of the 40th International Conference on Machine Learning (ICML) 2023 (Oral Presentation)
[Arxiv]
Publications
Proceedings of the 40th International Conference on Machine Learning (ICML) 2023 (Oral Presentation)
[Arxiv]
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
H. Bai, G. Canal, X. Du, J. Kwon, R. D Nowak, Y. LiProceedings of the 40th International Conference on Machine Learning (ICML) 2023 (to appear)
[Arxiv]
Reward-Mixing MDPs with Few Latent Contexts are Learnable
J. Kwon, Y. Efroni, C. Caramanis and S. MannorProceedings of the 40th International Conference on Machine Learning (ICML) 2023
[Arxiv]
Tractable Optimality in Episodic Latent MABs
J. Kwon, Y. Efroni, C. Caramanis and S. MannorProceedings of the 36th Neural Information Processing Systems (NeurIPS) 2022
[Arxiv] [Conference]
Coordinate Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
J. Kwon, Y. Efroni, C. Caramanis and S. MannorProceedings of the 39th International Conference on Machine Learning (ICML) 2022
[Arxiv] [Conference]
Reinforcement Learning in Reward-Mixing MDPs
J. Kwon, Y. Efroni, C. Caramanis and S. MannorProceedings of the 35th Neural Information Processing Systems (NeurIPS) 2021
[Arxiv] [Conference]
MLE and EM for Well-Separated Mixtures: Minimax Rates
J. Kwon and C. CaramanisWork in Progress
[Arxiv]
RL for Latent MDPs: Regret Guarantees and a Lower Bound
J. Kwon, Y. Efroni, C. Caramanis and S. MannorProceedings of the 35th Neural Information Processing Systems (NeurIPS) 2021 (Spotlight)
[Arxiv] [Conference] [RL Theory Seminar]
On the Computational and Statistical Complexity of Over-Parameterized Matrix Sensing
J. Zhuo, J. Kwon, N. Ho and C. CaramanisPreprint, 2021
[Arxiv]
On the Minimax Optimality of the EM Algorithm for Two-Component Mixed Linear Regression
J. Kwon, N. Ho and C. CaramanisProceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
[Arxiv] [Conference]
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians
J. Kwon and C. CaramanisProceedings of the 33rd Annual Conference on Learning Theory (COLT) 2020
[Arxiv] [Conference] [Video]
EM Converges for a Mixture of Many Linear Regressions
J. Kwon and C. CaramanisProceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
[Arxiv] [Conference]
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
J. Kwon, W. Qian, C. Caramanis, Y. Chen, and D. DamekProceedings of the 32nd Annual Conference on Learning Theory (COLT) 2019
[Arxiv] [Conference] [Video]