Yuda Song

Hi, I am first year PhD student at Machine Learning Department, Carnegie Mellon University . I am coadvise by Aarti Singh and Drew Bagnell. I am also closely working with Wen Sun. I finished my master degree in MLD, advised by Kris Kitani. I completed my undergraduate at UC San Diego with CS and Math double major and I was advised by Sicun Gao.

Semantic Scholar / Google Scholar / Email / CV / Github / Twitter

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.

Preprints

Provable Benefits of Representational Transfer in Reinforcement Learning
(alphabetical order) Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang

[code]

Publication

	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]
	Online No-regret Model-Based Meta RL for Personalized Navigation Yuda Song, Ye Yuan, Wen Sun, Kris Kitani L4DC, 2022.
	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]
	PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration Yuda Song, Wen Sun ICML, 2021 [code]
	Provably Efficient Model-based Policy Adaptation Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao ICML, 2020 [Project Page] [code]

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)

Service

Reviewer: AAAI (2021-), ICML (2021-), NeurIPS (2021-), ICLR (2022).

Source code from here