5 papers
Incentivizing Time-Aware Fairness in Data Sharing
Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim +4
In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the…
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou +15
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented compl…
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents
Arun Verma, Indrajit Saha, Makoto Yokoo +1
This paper considers a contextual bandit problem involving multiple agents, where a learner sequentially observes the contexts and the agent's reported arms, and then selects the a…
Active Human Feedback Collection via Neural Contextual Dueling Bandits
Arun Verma, Xiaoqiang Lin, Zhongxiang Dai +2
Collecting human preference feedback is often expensive, leading recent works to develop principled algorithms to select them more efficiently. However, these works assume that the…
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding
Zhaoxuan Wu, Zijian Zhou, Arun Verma +3
We propose TETRIS, a novel method that optimizes the total throughput of batch speculative decoding in multi-request settings. Unlike existing methods that optimize for a single re…