Jeongyeol Kwon

Orcid: 0000-0002-7910-7817

According to our database1, Jeongyeol Kwon authored at least 21 papers between 2018 and 2024.

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Bibliography

2024
Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions.
IEEE Trans. Inf. Theory, September, 2024

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing.
J. Mach. Learn. Res., 2024

RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation.
CoRR, 2024

Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments.
CoRR, 2024

On The Complexity of First-Order Methods in Stochastic Bilevel Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Prospective Side Information for Latent MDPs.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation.
CoRR, 2023

A Fully First-Order Method for Stochastic Bilevel Optimization.
Proceedings of the International Conference on Machine Learning, 2023

Reward-Mixing MDPs with Few Latent Contexts are Learnable.
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.
Proceedings of the International Conference on Machine Learning, 2023

2022
Tractable Optimality in Episodic Latent MABs.
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.
Proceedings of the International Conference on Machine Learning, 2022

2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound.
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.
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.
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.
CoRR, 2020

The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians.
Proceedings of the Conference on Learning Theory, 2020

EM Converges for a Mixture of Many Linear Regressions.
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.
Proceedings of the Conference on Learning Theory, 2019

2018
Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression.
CoRR, 2018


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