Yifang Chen 陈一方

Ph.D student, Paul G. Allen School of Computer Science & Engineering, University of Washington Email: yifangc at cs dot washington dot edu Google Scholar Instagram Twitter

About me

I am currently a forth year Ph.D student in computer science and engineering from University of Washington. I am very fortunate to be advised by Prof. Kevin Jamieson and Prof. Simon Shaolei Du.

My general research interests lie in designing practical and adaptive machine learning algorithms with strong theoretical guarantees. One of my main focus is on corrupted and non-stationary online decision setting. Recently I am growing my interest in applying interactive learning techniques in large-scale deep learning models (Computer Vision, LLM, multi-modal) to improve the training and label efficiency. Particularly, from the empirical side, I am interested in doing a through investigation on how modern pre-trained models can help downstream data collection and how the human-in-the-loop algorithm can further improve the large scale models. From the theoretical side, I am interested in designing online/active learning algorithm beyond the linear setting, and combine that with the deep representation learning theory.

Prior to starting my PhD study, I did my master and undergrad in electrical engineering from University of Southern California advised by Prof. Haipeng Luo. I want to specially thank him as well as my colleague Chen-Yu Wei, who led me into the world of learning theory from nowhere.

Research

My research interests include

• Online learning and bandits

• Reinforcement learning and control theory

• Active learning and its application in large-scale models

• Representation learning

• AI for interdisciplinary applications (eg. Quantum, biomed, econ)

Representative publications (reverse chronological)

1. [Preprint][Github] LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning.
   Jifan Zhang, Yifang Chen (equal contribution), Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin Jamieson, Robert D Nowak
   

2. [ICML22] Active Multi-Task Representation Learning.
   Yifang Chen, Simon Du, Kevin Jamieson.
   

3. [ICML22] First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. (Long presentation)
   Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
   

4. [ICML22] Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes.
   Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
   

5. [NeurIPS’21] Corruption Robust Active Learning.
   Yifang Chen, Simon Du, Kevin Jamieson.
   

6. [ICML’21] Improved corruption robust algorithms for episodic reinforcement learning.
   Yifang Chen, Simon Du, Kevin Jamieson.
   

7. [COLT’19] A new algorithm for non-stationary contextual bandits: Efficient, optimal and parameter-free.
   Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei.