Yuda Song

Hi, I am an incoming PhD student at Machine Learning Department, Carnegie Mellon University , where I finished my master degree, advised by Kris Kitani. I completed my undergraduate at UCSD with CS and Math double major and I am very fortunate to be advised by Sicun Gao at UCSD. I am also closely working with Wen Sun.

Email / CV / Github / Twitter

Research

I am broadly interested in machine learning, especially reinforcement learning. I am interested in developing algorithms with theoretical guarantees and also testing their empirical counterparts in practice.

Preprints

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