3 citations · 4 across the 6 of their papers we have counts for
3 papers · 1 filter
Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
Xiangyu Chang, Manyi Yao, Srikanth V. Krishnamurthy +5
In Asynchronous Federated Learning (AFL), the central server immediately updates the global model with each arriving client's contribution. As a result, clients perform their local…
When and How Unlabeled Data Provably Improve In-Context Learning
Yingcong Li, Xiangyu Chang, Muti Kara +3
Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a cano…
Provable Benefits of Task-Specific Prompts for In-context Learning
Xiangyu Chang, Yingcong Li, Muti Kara +2
The in-context learning capabilities of modern language models have motivated a deeper mathematical understanding of sequence models. A line of recent work has shown that linear at…