Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. - Zi-Hao Qiu; Quanqi Hu; Yongjian Zhong; Lijun Zhang 005; Tianbao Yang (ICML, 2022)

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. - Quanqi Hu; Yongjian Zhong; Tianbao Yang (NeurIPS, 2022)

Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. - Quanqi Hu; Zi-Hao Qiu; Zhishuai Guo; Lijun Zhang 005; Tianbao Yang (ICML, 2023)

Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. - Zi-Hao Qiu; Quanqi Hu; Zhuoning Yuan; Denny Zhou; Lijun Zhang 005; Tianbao Yang (ICML, 2023)

Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. - Quanqi Hu; Dixian Zhu; Tianbao Yang (NeurIPS, 2023)

Enhancing 3D Cardiac CT Segmentation with Latent Diffusion Model and Self-Supervised Learning. - Quanqi Hu; Ashok Vardhan Addala; Masaki Ikuta; Ravi Soni; Gopal Avinash (ML4H@NeurIPS, 2024)

Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. - Quanqi Hu; Qi Qi 006; Zhaosong Lu; Tianbao Yang (NeurIPS, 2024)

Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. - Qi Qi 006; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 2024)

Optimal large-scale stochastic optimization of NDCG surrogates for deep learning. - Zi-Hao Qiu; Quanqi Hu; Yongjian Zhong; Wei-Wei Tu; Lijun Zhang 005; Tianbao Yang (Mach. Learn., 2025)

Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints. - Ming Yang; Gang Li; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 2025)

Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization. - Xingyu Chen; Bokun Wang; Min Yang 007; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 2025)