Jeongyeol Kwon

42 papers A* 15A 3Journal 23Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Imbalanced Gradients in RL Post-Training of Multi-Task LLMs. EACL (Findings) Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, Yonathan Efroni
2025	A*	`conf`	A Classification View on Meta Learning Bandits. ICML Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar
2025	J	`jnl`	A Classification View on Meta Learning Bandits. CoRR Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar
2025	J	`jnl`	Imbalanced Gradients in RL Post-Training of Multi-Task LLMs. CoRR Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, Yonathan Efroni
2025	A*	`conf`	Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. COLT Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun
2025	J	`jnl`	Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. CoRR Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun
2025	A	`conf`	Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way. AISTATS Jeongyeol Kwon, Luke Dotson, Yudong Chen, Qiaomin Xie
2024	J	`jnl`	Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. CoRR Jeongyeol Kwon, Liu Yang, Robert D. Nowak, Josiah Hanna
2024	J	`jnl`	Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. IEEE Trans. Inf. Theory Jeongyeol Kwon, Wei Qian, Yudong Chen, Constantine Caramanis, Damek Davis, Nhat Ho
2024	A*	`conf`	On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. ICLR Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak
2024	A*	`conf`	On The Complexity of First-Order Methods in Stochastic Bilevel Optimization. ICML Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu
2024	J	`jnl`	On the Complexity of First-Order Methods in Stochastic Bilevel Optimization. CoRR Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu
2024	J	`jnl`	On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing. J. Mach. Learn. Res. Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis
2024	A*	`conf`	Prospective Side Information for Latent MDPs. ICML Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis
2024	A*	`conf`	RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation. NeurIPS Jeongyeol Kwon, Shie Mannor, Constantine Caramanis, Yonathan Efroni
2024	J	`jnl`	RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation. CoRR Jeongyeol Kwon, Shie Mannor, Constantine Caramanis, Yonathan Efroni
2024	J	`jnl`	Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way. CoRR Jeongyeol Kwon, Luke Dotson, Yudong Chen, Qiaomin Xie
2023	A*	`conf`	A Fully First-Order Method for Stochastic Bilevel Optimization. ICML Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak
2023	J	`jnl`	A Fully First-Order Method for Stochastic Bilevel Optimization. CoRR Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak
2023	A*	`conf`	Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. ICML Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li
2023	J	`jnl`	Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. CoRR Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li
2023	J	`jnl`	On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. CoRR Jeongyeol Kwon, Dohyun Kwon, Steve Wright, Robert D. Nowak
2023	J	`jnl`	Prospective Side Information for Latent MDPs. CoRR Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis
2023	A*	`conf`	Reward-Mixing MDPs with Few Latent Contexts are Learnable. ICML Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2022	A*	`conf`	Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. ICML Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2022	J	`jnl`	Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. CoRR Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2022	J	`jnl`	Reward-Mixing MDPs with a Few Latent Contexts are Learnable. CoRR Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2022	A*	`conf`	Tractable Optimality in Episodic Latent MABs. NeurIPS Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2022	J	`jnl`	Tractable Optimality in Episodic Latent MABs. CoRR Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2021	A	`conf`	On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. AISTATS Jeongyeol Kwon, Nhat Ho, Constantine Caramanis
2021	J	`jnl`	On the computational and statistical complexity of over-parameterized matrix sensing. CoRR Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis
2021	A*	`conf`	RL for Latent MDPs: Regret Guarantees and a Lower Bound. NeurIPS Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2021	J	`jnl`	RL for Latent MDPs: Regret Guarantees and a Lower Bound. CoRR Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2021	A*	`conf`	Reinforcement Learning in Reward-Mixing MDPs. NeurIPS Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2021	J	`jnl`	Reinforcement Learning in Reward-Mixing MDPs. CoRR Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
2020	J	`jnl`	EM Algorithm is Sample-Optimal for Learning Mixtures of Well-Separated Gaussians. CoRR Jeongyeol Kwon, Constantine Caramanis
2020	A	`conf`	EM Converges for a Mixture of Many Linear Regressions. AISTATS Jeongyeol Kwon, Constantine Caramanis
2020	J	`jnl`	On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. CoRR Jeongyeol Kwon, Nhat Ho, Constantine Caramanis
2020	A*	`conf`	The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. COLT Jeongyeol Kwon, Constantine Caramanis
2019	J	`jnl`	EM Converges for a Mixture of Many Linear Regressions. CoRR Jeongyeol Kwon, Constantine Caramanis
2019	A*	`conf`	Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. COLT Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis
2018	J	`jnl`	Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression. CoRR Jeongyeol Kwon, Constantine Caramanis