39 citations · 79 across the 20 of their papers we have counts for
15 papers · 1 filter
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning
Yifang Chen, Shuohang Wang, Ziyi Yang +6
Reinforcement learning with human feedback (RLHF), as a widely adopted approach in current large language model pipelines, is \textit{bottlenecked by the size of human preference d…
Decoding-Time Language Model Alignment with Multiple Objectives
Ruizhe Shi, Yifang Chen, Yushi Hu +4
Aligning language models (LMs) to human preferences has emerged as a critical pursuit, enabling these models to better serve diverse user needs. Existing methods primarily focus on…
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning
Yiping Wang, Yifang Chen, Wendan Yan +4
Data selection has emerged as a core issue for large-scale visual-language model pretaining (e.g., CLIP), particularly with noisy web-curated datasets. Three main data selection ap…
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning
Yiping Wang, Yifang Chen, Wendan Yan +2
In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets. One widely adopted strategy…
Active Representation Learning for General Task Space with Applications in Robotics
Yifang Chen, Yingbing Huang, Simon S. Du +2
Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal r…
Improved Active Multi-Task Representation Learning via Lasso
Yiping Wang, Yifang Chen, Kevin Jamieson +1
To leverage the copious amount of data from source tasks and overcome the scarcity of the target task samples, representation learning based on multi-task pretraining has become a…
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Jifan Zhang, Yifang Chen, Gregory Canal +8
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning…
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
Yifang Chen, Karthik Sankararaman, Alessandro Lazaric +6
Active learning with strong and weak labelers considers a practical setting where we have access to both costly but accurate strong labelers and inaccurate but cheap predictions pr…
Active Multi-Task Representation Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
To leverage the power of big data from source tasks and overcome the scarcity of the target task samples, representation learning based on multi-task pretraining has become a stand…
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew Wagenmaker, Yifang Chen, Max Simchowitz +2
Reward-free reinforcement learning (RL) considers the setting where the agent does not have access to a reward function during exploration, but must propose a near-optimal policy f…
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Andrew Wagenmaker, Yifang Chen, Max Simchowitz +2
Obtaining first-order regret bounds -- regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance -- is a core q…
Corruption Robust Active Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. In this setting, every time before the lear…
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
We study episodic reinforcement learning under unknown adversarial corruptions in both the rewards and the transition probabilities of the underlying system. We propose new algorit…
Fair Contextual Multi-Armed Bandits: Theory and Experiments
Yifang Chen, Alex Cuellar, Haipeng Luo +3
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting…
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo +1
We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\ma…