4 citations · 9 across the 9 of their papers we have counts for
10 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…
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…
Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions
Quanqi Hu, Qi Qi, Zhaosong Lu +1
In this paper, we study a class of non-smooth non-convex problems in the form of , where both a…
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
Quanqi Hu, Dixian Zhu, Tianbao Yang
This paper investigates new families of compositional optimization problems, called on-mooth eakly-onvex…
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo +2
In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve lower level problems and have important applications in machine learn…
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan +3
In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of usi…
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
Quanqi Hu, Yongjian Zhong, Tianbao Yang
In this paper, we study multi-block min-max bilevel optimization problems, where the upper level is non-convex strongly-concave minimax objective and the lower level is a strongly…
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong +2
NDCG, namely Normalized Discounted Cumulative Gain, is a widely used ranking metric in information retrieval and machine learning. However, efficient and provable stochastic method…