Jeongyeol Kwon

Wei Qian

Yudong Chen

Damek Davis

Nhat Ho

IEEE Trans. Inf. Theory, September, 2024

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing.

[BibT_eX]

[DOI]

Jiacheng Zhuo

Nhat Ho

J. Mach. Learn. Res., 2024

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way.

[BibT_eX]

[DOI]

CoRR, 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

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

[BibT_eX]

[DOI]

Dohyun Kwon

Hanbaek Lyu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Prospective Side Information for Latent MDPs.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

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

[BibT_eX]

[DOI]

CoRR, 2023

A Fully First-Order Method for Stochastic Bilevel Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Reward-Mixing MDPs with Few Latent Contexts are Learnable.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Tractable Optimality in Episodic Latent MABs.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Nhat Ho

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.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2020

EM Converges for a Mixture of Many Linear Regressions.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Wei Qian

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.

[BibT_eX]

[DOI]