3 citations · 3 across the 6 of their papers we have counts for
7 papers
Class-Proportional Coreset Selection for Difficulty-Separable Data
Elisa Tsai, Haizhong Zheng, Atul Prakash
High-quality training data is essential for building reliable and efficient machine learning systems. One-shot coreset selection addresses this by pruning the dataset while maintai…
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
Haizhong Zheng, Yang Zhou, Brian R. Bartoldson +4
Reinforcement learning, such as PPO and GRPO, has powered recent breakthroughs in LLM reasoning. Scaling rollout to sample more prompts enables models to selectively use higher-qua…
Harmful Terms and Where to Find Them: Measuring and Modeling Unfavorable Financial Terms and Conditions in Shopping Websites at Scale
Elisa Tsai, Neal Mangaokar, Boyuan Zheng +2
Terms and conditions for online shopping websites often contain terms that can have significant financial consequences for customers. Despite their impact, there is currently no co…
ELFS: Label-Free Coreset Selection with Proxy Training Dynamics
Haizhong Zheng, Elisa Tsai, Yifu Lu +4
High-quality human-annotated data is crucial for modern deep learning pipelines, yet the human annotation process is both costly and time-consuming. Given a constrained human label…
Plato: Plan to Efficiently Decode for Large Language Model Inference
Shuowei Jin, Xueshen Liu, Yongji Wu +7
Large language models (LLMs) have achieved remarkable success in natural language tasks, but their inference incurs substantial computational and memory overhead. To improve effici…
Learn To be Efficient: Build Structured Sparsity in Large Language Models
Haizhong Zheng, Xiaoyan Bai, Xueshen Liu +4
Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in…