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Jeongyeol Kwon
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2020 – today
- 2024
- [j2]Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing. J. Mach. Learn. Res. 25: 169:1-169:47 (2024) - [j1]Jeongyeol Kwon
, Wei Qian
Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. IEEE Trans. Inf. Theory 70(9): 6519-6546 (2024) - [c14]Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak:
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. ICLR 2024 - [c13]Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis:
Prospective Side Information for Latent MDPs. ICML 2024 - [c12]Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu:
On The Complexity of First-Order Methods in Stochastic Bilevel Optimization. ICML 2024 - [i17]Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu:
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization. CoRR abs/2402.07101 (2024) - [i16]Jeongyeol Kwon, Liu Yang, Robert D. Nowak, Josiah Hanna:
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. CoRR abs/2402.07102 (2024) - [i15]Jeongyeol Kwon, Shie Mannor, Constantine Caramanis, Yonathan Efroni:
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation. CoRR abs/2406.01389 (2024) - 2023
- [c11]Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. ICML 2023: 1454-1471 - [c10]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reward-Mixing MDPs with Few Latent Contexts are Learnable. ICML 2023: 18057-18082 - [c9]Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak:
A Fully First-Order Method for Stochastic Bilevel Optimization. ICML 2023: 18083-18113 - [i14]Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak:
A Fully First-Order Method for Stochastic Bilevel Optimization. CoRR abs/2301.10945 (2023) - [i13]Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. CoRR abs/2306.09158 (2023) - [i12]Jeongyeol Kwon, Dohyun Kwon, Steve Wright, Robert D. Nowak:
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation. CoRR abs/2309.01753 (2023) - [i11]Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis:
Prospective Side Information for Latent MDPs. CoRR abs/2310.07596 (2023) - 2022
- [c8]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. ICML 2022: 11772-11789 - [c7]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Tractable Optimality in Episodic Latent MABs. NeurIPS 2022 - [i10]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. CoRR abs/2201.12700 (2022) - [i9]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reward-Mixing MDPs with a Few Latent Contexts are Learnable. CoRR abs/2210.02594 (2022) - [i8]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Tractable Optimality in Episodic Latent MABs. CoRR abs/2210.03528 (2022) - 2021
- [c6]Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. AISTATS 2021: 1405-1413 - [c5]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reinforcement Learning in Reward-Mixing MDPs. NeurIPS 2021: 2253-2264 - [c4]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
RL for Latent MDPs: Regret Guarantees and a Lower Bound. NeurIPS 2021: 24523-24534 - [i7]Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the computational and statistical complexity of over-parameterized matrix sensing. CoRR abs/2102.02756 (2021) - [i6]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
RL for Latent MDPs: Regret Guarantees and a Lower Bound. CoRR abs/2102.04939 (2021) - [i5]Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reinforcement Learning in Reward-Mixing MDPs. CoRR abs/2110.03743 (2021) - 2020
- [c3]Jeongyeol Kwon, Constantine Caramanis:
EM Converges for a Mixture of Many Linear Regressions. AISTATS 2020: 1727-1736 - [c2]Jeongyeol Kwon, Constantine Caramanis:
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. COLT 2020: 2425-2487 - [i4]Jeongyeol Kwon, Constantine Caramanis:
EM Algorithm is Sample-Optimal for Learning Mixtures of Well-Separated Gaussians. CoRR abs/2002.00329 (2020) - [i3]Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. CoRR abs/2006.02601 (2020)
2010 – 2019
- 2019
- [c1]Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis:
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. COLT 2019: 2055-2110 - [i2]Jeongyeol Kwon, Constantine Caramanis:
EM Converges for a Mixture of Many Linear Regressions. CoRR abs/1905.12106 (2019) - 2018
- [i1]Jeongyeol Kwon, Constantine Caramanis:
Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression. CoRR abs/1810.05752 (2018)
Coauthor Index
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