Yifang Chen 陈一方

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

About Me

I am currently a fifth-year Ph.D. student in Computer Science and Engineering at the University of Washington. I am fortunate to be advised by Prof. Kevin Jamieson and Prof. Simon Shaolei Du.

My research focuses on algorithmic data-and-label-efficient learning from both empirical and theoretical perspectives. Specifically:

• Empirical side: I investigate how to curate data in an open-world setting to train general-purpose large models and design label-efficient algorithms to adapt pretrained models to specific downstream tasks.

• Theoretical side: I design online/active learning algorithms beyond the well-specified setting and combine them with deep representation learning theory.

Check out our website LabelTrain.ai, an ongoing open-source project for experimentally exploring the strengths and weaknesses of label-efficient learning algorithms.

In my early years, I designed practical and adaptive machine learning algorithms with strong theoretical guarantees, focusing on corrupted and non-stationary online decision-making settings.

Prior to starting my Ph.D., I completed my master's and undergraduate degrees in Electrical Engineering at the University of Southern California, advised by Prof. Haipeng Luo. I want to especially thank him and my colleague Chen-Yu Wei, who introduced me to the world of learning theory.

I am on the job market this year! Looking for both industrial and academic opportunities!

Research Interests

• Active learning, data selection, experimental design

• Representation learning

• Online learning, bandits, Reinforcement learning theory

• Experimental design for scientific applications

Selected Publications

1. [NeurIPS24] CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning. (Spotlight)
   Yiping Wang*, Yifang Chen*, Wendan Yan, Alex Fang, Wenjin Zhou, Simon Du, Kevin Jamieson.

2. [ACLFindings24] Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models.
   Gantavya Bhatt*, Yifang Chen*, Arnav M. Das*, Jifan Zhang*, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak

3. [Journal of DMLR] LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. [Github]
   Jifan Zhang*, Yifang Chen*, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin Jamieson, Robert D Nowak

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

5. [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

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