6 citations · 24 across the 8 of their papers we have counts for
4 papers · 1 filter
Data Shifts Hurt CoT: A Theoretical Study
Lang Yin, Debangshu Banerjee, Gagandeep Singh
Chain of Thought (CoT) has been applied to various large language models (LLMs) and proven to be effective in improving the quality of outputs. In recent studies, transformers are…
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
Yuzheng Hu, Ruicheng Xian, Qilong Wu +3
Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recen…
Fair and Optimal Classification via Post-Processing
Ruicheng Xian, Lang Yin, Han Zhao
To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it of…
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang +5
Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to…