Tianbao Yang

ORCID

Publication Activity (10 Years)

Years Active: 2009-2023
Publications (10 Years): 214

Top Venues

Publications

Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. CoRR (2023)
Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
FeDXL: Provable Federated Learning for Deep X-Risk Optimization. ICML (2023)
Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. ICML (2023)
Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. CoRR (2023)
Quanqi Hu, Dixian Zhu, Tianbao Yang
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. CoRR (2023)
Ryan King, Tianbao Yang, Bobak Mortazavi
Multimodal Pretraining of Medical Time Series and Notes. CoRR (2023)
Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. CoRR (2023)
Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
Learning Unnormalized Statistical Models via Compositional Optimization. CoRR (2023)
Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. NeurIPS (2023)
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang
LibAUC: A Deep Learning Library for X-Risk Optimization. KDD (2023)
Gang Li, Wei Tong, Tianbao Yang
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness. NeurIPS (2023)
Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. J. Mach. Learn. Res. 24 (2023)
Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. ICML (2023)
Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. J. Mach. Learn. Res. 24 (2023)
Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. ICML (2023)
Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
Generalization Analysis for Contrastive Representation Learning. CoRR (2023)
Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. NeurIPS (2023)
Ryan King, Tianbao Yang, Bobak J. Mortazavi
Multimodal Pretraining of Medical Time Series and Notes. ML4H@NeurIPS (2023)
Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. CoRR (2023)
Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
Generalization Analysis for Contrastive Representation Learning. ICML (2023)
Bokun Wang, Tianbao Yang
ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization. CoRR (2023)
Jianzhi Xv, Gang Li, Tianbao Yang
AUC-mixup: Deep AUC Maximization with Mixup. CoRR (2023)
Lijun Zhang, Peng Zhao, Tianbao Yang, Zhi-Hua Zhou
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. CoRR (2023)
Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. CoRR (2023)
Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
Learning Unnormalized Statistical Models via Compositional Optimization. ICML (2023)
Yao Yao, Qihang Lin, Tianbao Yang
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS (2023)
Quanqi Hu, Dixian Zhu, Tianbao Yang
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. NeurIPS (2023)
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang
LibAUC: A Deep Learning Library for X-Risk Optimization. CoRR (2023)
Dixian Zhu, Yiming Ying, Tianbao Yang
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. ICML (2023)
Tianbao Yang, Yiming Ying
AUC Maximization in the Era of Big Data and AI: A Survey. ACM Comput. Surv. 55 (8) (2023)
Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Federated Compositional Deep AUC Maximization. NeurIPS (2023)
Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. CoRR (2023)
Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Federated Compositional Deep AUC Maximization. CoRR (2023)
Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang
Compositional Training for End-to-End Deep AUC Maximization. ICLR (2022)
Quanqi Hu, Yongjian Zhong, Tianbao Yang
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. CoRR (2022)
Tianbao Yang, Yiming Ying
AUC Maximization in the Era of Big Data and AI: A Survey. CoRR (2022)
Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. ICML (2022)
Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, Hao Sun, Tianbao Yang, Changwei Tan
SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification. Remote. Sens. 14 (22) (2022)
Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. CoRR (2022)
Quanqi Hu, Yongjian Zhong, Tianbao Yang
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. NeurIPS (2022)
Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. CoRR (2022)
Bokun Wang, Tianbao Yang
Finite-Sum Compositional Stochastic Optimization: Theory and Applications. CoRR (2022)
Yao Yao, Qihang Lin, Tianbao Yang
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR (2022)
Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang
Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR (2022)
Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. ICML (2022)
Yao Yao, Qihang Lin, Tianbao Yang
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR (2022)
Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. CoRR (2022)
Tianbao Yang
Algorithmic Foundation of Deep X-Risk Optimization. CoRR (2022)
Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. CoRR (2022)
Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang
Stochastic Constrained DRO with a Complexity Independent of Sample Size. CoRR (2022)
Bokun Wang, Tianbao Yang
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. ICML (2022)
Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. CoRR (2022)
Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. NeurIPS (2022)
Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. ICML (2022)
Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. NeurIPS (2022)
Yao Yao, Qihang Lin, Tianbao Yang
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS (2022)
Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. 37 (3) (2022)
Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. AISTATS (2022)
Zhuoning Yuan, Yuexin Wu, Zihao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. CoRR (2022)
Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. ICML (2022)
Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
FedX: Federated Learning for Compositional Pairwise Risk Optimization. CoRR (2022)
Dixian Zhu, Xiaodong Wu, Tianbao Yang
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices. CoRR (2022)
Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
A Simple yet Universal Strategy for Online Convex Optimization. ICML (2022)
Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. ICML (2022)
Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. CoRR (2022)
Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML (2021)
Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
Revisiting Smoothed Online Learning. NeurIPS (2021)
Dixian Zhu, Tianbao Yang
A Unified DRO View of Multi-class Loss Functions with top-N Consistency. CoRR (2021)
Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. CoRR (2021)
Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR (2021)
Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
Online Convex Optimization with Continuous Switching Constraint. NeurIPS (2021)
Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
Revisiting Smoothed Online Learning. CoRR (2021)
Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. CoRR (2021)
Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR (2021)
Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
Online Convex Optimization with Continuous Switching Constraint. CoRR (2021)
Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. ICCV (2021)
Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. CoRR (2021)
Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS (2021)
Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. CoRR (2021)
Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. ICML (2021)
Zhishuai Guo, Tianbao Yang
Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization. CoRR (2021)
Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. NeurIPS (2021)
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. 22 (2021)
Yaohui Zeng, Tianbao Yang, Patrick Breheny
Hybrid safe-strong rules for efficient optimization in lasso-type problems. Comput. Stat. Data Anal. 153 (2021)
Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. NeurIPS (2021)
Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
Memory-based Optimization Methods for Model-Agnostic Meta-Learning. CoRR (2021)
Tianbao Yang
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities. CoRR (2021)
Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
A Simple yet Universal Strategy for Online Convex Optimization. CoRR (2021)
Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR (2021)
Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang
Adversarial Localized Energy Network for Structured Prediction. AAAI (2020)
Lijun Zhang, Shiyin Lu, Tianbao Yang
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. AISTATS (2020)
Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
Stochastic AUC Maximization with Deep Neural Networks. ICLR (2020)
Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu
Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR (2020)
Yan Yan, Xin Man, Tianbao Yang
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise. CoRR (2020)
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR (2020)
Runchao Ma, Qihang Lin, Tianbao Yang
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML (2020)
Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
Accelerating Deep Learning with Millions of Classes. ECCV (23) (2020)
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML (2020)
Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi
Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent. J. Parallel Distributed Comput. 143 (2020)
Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang
A Simple and Effective Framework for Pairwise Deep Metric Learning. ECCV (27) (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Tianbao Yang