4 citations · 5 across the 17 of their papers we have counts for
6 papers · 1 filter
FairSSL: Fair Multimodal Self-Supervised Learning
Jiaee Cheong, Abtin Mogharabin, Paul Liang +2
Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-relevant information. We argue that this…
GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation
Zhiyu Wang, Yang Liu, Hatice Gunes
Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatment planning, particularly for pat…
Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness
Mojtaba Kolahdouzi, Hatice Gunes, Ali Etemad
We study whether and how the choice of optimization algorithm can impact group fairness in deep neural networks. Through stochastic differential equation analysis of optimization d…
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
Jiaee Cheong, Aditya Bangar, Sinan Kalkan +1
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitas…
Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
Joseph Cameron, Jiaee Cheong, Micol Spitale +1
Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorit…
Uncertainty-based Fairness Measures
Selim Kuzucu, Jiaee Cheong, Hatice Gunes +1
Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means f…