Publication

Label efficient learning on large-scale models (Fine-tuning stage)

1. [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
   

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

3. [Preprint] Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler.
   Yifang Chen, Karthik Sankararaman, Alessandro Lazaric, Matteo Pirotta, Dmytro Karamshuk, Qifan Wang, Karishma Mandyam, Sinong Wang, Han Fang
   

Adaptive Representation Learning (Pre-training stage)

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.
   It's previous version is [Preprint] Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning.
   

2. [NeurIPS’23][Github] Active Representation Learning for General Task Space with Applications in Robotics.
   Yifang Chen, Yingbing Huang, Simon S. Du, Kevin Jamieson, Guanya Shi (Yingbing is an undergrad mentored by me.)
   

3. [ICML23] Improved Active Multi-Task Representation Learning via Lasso.
   Yiping Wang, Yifang Chen, Simon Du, Kevin Jamieson. (Yiping is an undergrad mentored by me.)
   

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

LLM and RLHF

1. [NeurIPS24] Decoding-Time Language Model Alignment with Multiple Objectives.
   Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A. Smith, Simon Shaolei Du (Ruizhe is an undergrad mentored by me.)
   

Adaptive and robust interactive learning algorithms (bandits, RL, active learning)

1. [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
   

2. [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
   

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

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

5. [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.
   

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

Online learning and active learning for interdisciplinary applications

1. [Neurips 2022 workshop CDS and CML4Impact] Causal Bandits: Online Decision-Making in Endogeneous Settings.
   Jingwen Zhang, Yifang Chen, Amandeep Singh (Econ,bussiness)
   

2. [ICML22 ReALML workshop ] A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior.
   Mingyu Lu, Yifang Chen, Su-In Lee. (Health, compBio)
   

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

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

5. [Preprint] More Practical and Adaptive Algorithms for Online Quantum State Learning.
   Yifang Chen, Xin Wang. (Physics)