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 second 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. Recently I am growing my interest in applying active learning techinques in large-scale deep learning models to improve the training efficiency. I am also interested in applying interactive learning algorithms for scientific studies like healthcare, quantum computing and economics.

Prior to starting my PhD study, I did my master and undergrad in electrical engineering from Unversity 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

• AI for science (eg. Quantum, biomed, econ)

Preprints

1. [Preprint] A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior.
   Mingyu Lu, Yifang Chen, Su-In Lee
   

Publications (reverse chronological)

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

2. [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
   

3. [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
   

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

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

6. [UAI’20] Fair contextual multi-armed bandits: Theory and experiments.
   Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, Stefanos Nikolaidis.
   

7. [HRI’20] Multi-armed bandits with fairness constraints for distributing resources to human teammates.
   Houston Claure, Yifang Chen, Jignesh Modi, Malte Jung, Stefanos Nikolaidis.
   

8. [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.
   

Some old preprints

1. [Preprint] More Practical and Adaptive Algorithms for Online Quantum State Learning.
   Yifang Chen, Xin Wang.
   

2. [Preprint] Online and bandit algorithms for nonstationary stochastic saddle-point optimization.
   Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian, Prasant Mohapatra