6 citations · 8 across the 6 of their papers we have counts for
6 papers
BiasCause: Evaluate Socially Biased Causal Reasoning of Large Language Models
Tian Xie, Tongxin Yin, Vaishakh Keshava +2
While large language models (LLMs) play increasingly significant roles in society, research shows they continue to generate content that reflects social bias against sensitive grou…
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework
Jiayu Mao, Tongxin Yin, Aylin Yener +1
Federated Learning (FL) is a distributed machine learning framework that inherently allows edge devices to maintain their local training data, thus providing some level of privacy.…
Fair Classifiers that Abstain without Harm
Tongxin Yin, Jean-François Ton, Ruocheng Guo +3
In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from pred…
Federated Learning with Reduced Information Leakage and Computation
Tongxin Yin, Xuwei Tan, Xueru Zhang +2
Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although l…
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
Kun Jin, Tongxin Yin, Zhongzhu Chen +4
We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distribu…
Long-Term Fairness with Unknown Dynamics
Tongxin Yin, Reilly Raab, Mingyan Liu +1
While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in t…