163 citations · 1.2k across the 128 of their papers we have counts for
17 papers · 1 filter
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Yao Yao, Qihang Lin, Tianbao Yang
As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected…
Fairness via Adversarial Attribute Neighbourhood Robust Learning
Qi Qi, Shervin Ardeshir, Yi Xu +1
Improving fairness between privileged and less-privileged sensitive attribute groups (e.g, {race, gender}) has attracted lots of attention. To enhance the model performs uniformly…
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Zhishuai Guo, Rong Jin, Jiebo Luo +1
In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective…
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Qi Qi, Jiameng Lyu, Kung sik Chan +2
Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention…
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
Wei Jiang, Gang Li, Yibo Wang +2
Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintai…
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu, Limei Wang, Bokun Wang +3
Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding…