1 citations · 1 across the 8 of their papers we have counts for
8 papers
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
Jiancheng Zhang, Meiqing Li, Qi Zhang +1
Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly…
Online Finetuning Decision Transformers with Pure RL Gradients
Junkai Luo, Yinglun Zhu
Decision Transformers (DTs) have emerged as a powerful framework for sequential decision making by formulating offline reinforcement learning (RL) as a sequence modeling problem. H…
Interactive Machine Learning: From Theory to Scale
Yinglun Zhu
Machine learning has achieved remarkable success across a wide range of applications, yet many of its most effective methods rely on access to large amounts of labeled data or exte…
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
Bowen Zuo, Yinglun Zhu
Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniforml…
Efficient Sparse PCA via Block-Diagonalization
Alberto Del Pia, Dekun Zhou, Yinglun Zhu
Sparse Principal Component Analysis (Sparse PCA) is a pivotal tool in data analysis and dimensionality reduction. However, Sparse PCA is a challenging problem in both theory and pr…
Efficient Sequential Decision Making with Large Language Models
Dingyang Chen, Qi Zhang, Yinglun Zhu
This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…
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…