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

**J. Kwon**, L. Yang, R. Nowak and J. Hanna

arXiv Preprint

- Postdoctorate in University of Wisconsin at Madison, 2022 ~ Present
- Ph.D in The University of Texas at Austin, 2017 ~ 2022
- B.S. in Korea, Seoul National University
*(summa cum laude)*, 2016

- Wisconsin Institute for Discovery, University of Wisconsin-Madison
- Postdoctorate (with Prof. Robert Nowak)

- Decision, Information, and Communications Engineering (DICE), The University of Texas at Austin
- Graduate Research Assistant (with Prof. Constantine Caramanis)

- Perceptron and Intelligence Laboratory (PIL), Seoul National University
- Research Internship (with Prof. Jin Young Choi)

- Alegion Inc., 2019.6 ~ 2019.8
- Research Internship
- Research on the automation of human annotation

- Scientific Analog, 2015.5 ~ 2016.6
- Research and Development Engineer
- Program Developer for Mixed Circuit Simulator

- Redduck, Inc., 2011.2 ~ 2013.12
- PC Online Game Client Programmer

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

**J. Kwon**, L. Yang, R. Nowak and J. Hanna

arXiv Preprint### On the Complexity of First-Order Methods in Stochastic Bilevel Optimization

**J. Kwon**, D. Kwon and H. Lyu

arXiv Preprint### Prospective Side Information for Latent MDPs

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

arXiv Preprint### On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation

**J. Kwon**, D. Kwon, S. Wright and R. Nowak

Proceedings of the 12th International Conference on Learning Representations (ICLR) 2024 (Spotlight)### A Fully First-Order Method for Stochastic Bilevel Optimization

**J. Kwon**, D. Kwon, S. Wright and R. Nowak

Proceedings of the 40th International Conference on Machine Learning (ICML) 2023 (Oral Presentation)### Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection

H. Bai, G. Canal, X. Du, J. Kwon, R. D Nowak, Y. Li

Proceedings of the 40th International Conference on Machine Learning (ICML) 2023### Reward-Mixing MDPs with Few Latent Contexts are Learnable

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

Proceedings of the 40th International Conference on Machine Learning (ICML) 2023### Tractable Optimality in Episodic Latent MABs

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

Proceedings of the 36th Neural Information Processing Systems (NeurIPS) 2022### Coordinate Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

Proceedings of the 39th International Conference on Machine Learning (ICML) 2022### Reinforcement Learning in Reward-Mixing MDPs

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

Proceedings of the 35th Neural Information Processing Systems (NeurIPS) 2021### MLE and EM for Well-Separated Mixtures: Minimax Rates

**J. Kwon**and C. Caramanis

Work in Progress### RL for Latent MDPs: Regret Guarantees and a Lower Bound

**J. Kwon**, Y. Efroni, C. Caramanis and S. Mannor

Proceedings of the 35th Neural Information Processing Systems (NeurIPS) 2021 (Spotlight)### On the Computational and Statistical Complexity of Over-Parameterized Matrix Sensing

J. Zhuo, J. Kwon, N. Ho and C. Caramanis

Journal of Machine Learning Research (JMLR) 2024 (To appear)### On the Minimax Optimality of the EM Algorithm for Two-Component Mixed Linear Regression

**J. Kwon**, N. Ho and C. Caramanis

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021### The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians

**J. Kwon**and C. Caramanis

Proceedings of the 33rd Annual Conference on Learning Theory (COLT) 2020### EM Converges for a Mixture of Many Linear Regressions

**J. Kwon**and C. Caramanis

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020### Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression

**J. Kwon**, W. Qian, C. Caramanis, Y. Chen, and D. Damek

Proceedings of the 32nd Annual Conference on Learning Theory (COLT) 2019

- Computer Skills: C/C++, Python, MATLAB, LaTeX
- Specialty: Learning Theory, Reinforcement Learning, Optimization, Algorithm
- Language: Korean (Native), English, Japanese