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.

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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