Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression. - Jeongyeol Kwon; Constantine Caramanis (CoRR, 2018)

Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. - Jeongyeol Kwon; Wei Qian; Constantine Caramanis; Yudong Chen 001; Damek Davis (COLT, 2019)

EM Converges for a Mixture of Many Linear Regressions. - Jeongyeol Kwon; Constantine Caramanis (AISTATS, 2020)

The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. - Jeongyeol Kwon; Constantine Caramanis (COLT, 2020)

EM Algorithm is Sample-Optimal for Learning Mixtures of Well-Separated Gaussians. - Jeongyeol Kwon; Constantine Caramanis (CoRR, 2020)

On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. - Jeongyeol Kwon; Nhat Ho; Constantine Caramanis (AISTATS, 2021)

Reinforcement Learning in Reward-Mixing MDPs. - Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor (NeurIPS, 2021)

RL for Latent MDPs: Regret Guarantees and a Lower Bound. - Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor (NeurIPS, 2021)

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. - Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor (ICML, 2022)

Tractable Optimality in Episodic Latent MABs. - Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor (NeurIPS, 2022)

Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. - Haoyue Bai 001; Gregory Canal; Xuefeng Du; Jeongyeol Kwon; Robert D. Nowak; Yixuan Li 001 (ICML, 2023)

Reward-Mixing MDPs with Few Latent Contexts are Learnable. - Jeongyeol Kwon; Yonathan Efroni; Constantine Caramanis; Shie Mannor (ICML, 2023)

A Fully First-Order Method for Stochastic Bilevel Optimization. - Jeongyeol Kwon; Dohyun Kwon 002; Stephen Wright; Robert D. Nowak (ICML, 2023)

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. - Jeongyeol Kwon; Dohyun Kwon 002; Steve Wright; Robert D. Nowak (CoRR, 2023)

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. - Jeongyeol Kwon; Dohyun Kwon 002; Stephen Wright; Robert D. Nowak (ICLR, 2024)

Prospective Side Information for Latent MDPs. - Jeongyeol Kwon; Yonathan Efroni; Shie Mannor; Constantine Caramanis (ICML, 2024)

On The Complexity of First-Order Methods in Stochastic Bilevel Optimization. - Jeongyeol Kwon; Dohyun Kwon 002; Hanbaek Lyu (ICML, 2024)

RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation. - Jeongyeol Kwon; Shie Mannor; Constantine Caramanis; Yonathan Efroni (NeurIPS, 2024)

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. - Jeongyeol Kwon; Liu Yang 001; Robert D. Nowak; Josiah Hanna (CoRR, 2024)

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing. - Jiacheng Zhuo; Jeongyeol Kwon; Nhat Ho; Constantine Caramanis (J. Mach. Learn. Res., 2024)

Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. - Jeongyeol Kwon; Wei Qian; Yudong Chen 001; Constantine Caramanis; Damek Davis; Nhat Ho (IEEE Trans. Inf. Theory, 2024)

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way. - Jeongyeol Kwon; Luke Dotson; Yudong Chen 001; Qiaomin Xie (AISTATS, 2025)

Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. - J. Jon Ryu; Jeongyeol Kwon; Benjamin Koppe; Kwang-Sung Jun (COLT, 2025)

A Classification View on Meta Learning Bandits. - Mirco Mutti; Jeongyeol Kwon; Shie Mannor; Aviv Tamar (CoRR, 2025)