Research
I am broadly interested in machine learning, especially reinforcement learning.
I am current focusing on provably statistically and computationally efficient settings and algorithms in RL.
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Publication
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Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
Yuda Song*, Yifei Zhou*, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
ICLR, 2023.
[code]
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Representation Learning for General-sum Low-rank Markov Games
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang
ICLR, 2023.
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Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun
ICML, 2022.
[code]
[Talk at RL theory seminars]
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Online No-regret Model-Based Meta RL for Personalized Navigation
Yuda Song, Ye Yuan, Wen Sun, Kris Kitani
L4DC, 2022.
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Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani
ICLR, 2022. Oral presentation
[Project Page]
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PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
Yuda Song, Wen Sun
ICML, 2021
[code]
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Provably Efficient Model-based Policy Adaptation
Yuda Song, Aditi Mavalankar,
Wen Sun, Sicun Gao
ICML, 2020
[Project Page]
[code]
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Teaching Assistant
UCSD CSE291: Topics in Search and Optimization (Winter 2020)
UCSD CSE154: Deep Learning (Fall 2019)
UCSD CSE150: Introduction to AI: Search and Reasoning (Winter 2019, Spring 2020)
UCSD CSE30: Computer Organization and Systems Programming (Spring 2019, Winter 2018)
UCSD CSE11: Introduction to CS & OOP (Fall 2018)
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Service
Reviewer: ICML (2021-), NeurIPS (2021-), ICLR (2022-), AAAI (2021-2022).
Top Reviewer: NeurIPS 2022
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