Jeongyeol Kwon

According to our database, Jeongyeol Kwon authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments.

Jeongyeol Kwon, Liu Yang, Robert D. Nowak, Josiah Hanna

CoRR, 2024

On the Complexity of First-Order Methods in Stochastic Bilevel Optimization.

Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu

CoRR, 2024

2023

Prospective Side Information for Latent MDPs.

Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis

CoRR, 2023

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation.

Jeongyeol Kwon, Dohyun Kwon, Steve Wright, Robert D. Nowak

CoRR, 2023

A Fully First-Order Method for Stochastic Bilevel Optimization.

Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak

Proceedings of the International Conference on Machine Learning, 2023

Reward-Mixing MDPs with Few Latent Contexts are Learnable.

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Proceedings of the International Conference on Machine Learning, 2023

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

Proceedings of the International Conference on Machine Learning, 2023

2022

Tractable Optimality in Episodic Latent MABs.

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms.

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Proceedings of the International Conference on Machine Learning, 2022

2021

On the computational and statistical complexity of over-parameterized matrix sensing.

Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis

CoRR, 2021

RL for Latent MDPs: Regret Guarantees and a Lower Bound.

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reinforcement Learning in Reward-Mixing MDPs.

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression.

Jeongyeol Kwon, Nhat Ho, Constantine Caramanis

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

EM Algorithm is Sample-Optimal for Learning Mixtures of Well-Separated Gaussians.

Jeongyeol Kwon, Constantine Caramanis

CoRR, 2020

The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians.

Jeongyeol Kwon, Constantine Caramanis

Proceedings of the Conference on Learning Theory, 2020

EM Converges for a Mixture of Many Linear Regressions.

Jeongyeol Kwon, Constantine Caramanis

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression.

Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis

Proceedings of the Conference on Learning Theory, 2019

2018

Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression.

Jeongyeol Kwon, Constantine Caramanis

CoRR, 2018