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2020 – today
- 2021
[c4]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- [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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