6 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
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…