2 citations · 2 across the 7 of their papers we have counts for
8 papers
Learning to Rank with Top- Fairness
Boyang Zhang, Quanqi Hu, Mingxuan Sun +2
Fairness in ranking models is crucial, as disparities in exposure can disproportionately affect protected groups. Most fairness-aware ranking systems focus on ensuring comparable a…
Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
Xingyu Chen, Bokun Wang, Ming Yang +2
Finite-sum Coupled Compositional Optimization (FCCO), characterized by its coupled compositional objective structure, emerges as an important optimization paradigm for addressing a…
Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints
Ming Yang, Gang Li, Quanqi Hu +2
Constrained optimization with multiple functional inequality constraints has significant applications in machine learning. This paper examines a crucial subset of such problems whe…
A Note on Complexity for Two Classes of Structured Non-Smooth Non-Convex Compositional Optimization
Yao Yao, Qihang Lin, Tianbao Yang
This note studies numerical methods for solving compositional optimization problems, where the inner function is smooth, and the outer function is Lipschitz continuous, non-smooth,…
A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
Gang Li, Wendi Yu, Yao Yao +4
In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks, which involve collecting new data, training…
Multi-Output Distributional Fairness via Post-Processing
Gang Li, Qihang Lin, Ayush Ghosh +1
The post-processing approaches are becoming prominent techniques to enhance machine learning models' fairness because of their intuitiveness, low computational cost, and excellent…
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning
Qi Qi, Quanqi Hu, Qihang Lin +1
This paper studies learning fair encoders in a self-supervised learning (SSL) setting, in which all data are unlabeled and only a small portion of them are annotated with sensitive…
On Data Preconditioning for Regularized Loss Minimization
Tianbao Yang, Rong Jin, Shenghuo Zhu +1
In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization. It is…