Tianbao Yang - RankMe

330 papers A* 102A 12B 4Journal 182Unranked 27

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization. CoRR Xiyuan Wei, Linli Zhou, Bokun Wang, Chih-Jen Lin, Tianbao Yang
2026	J	`jnl`	Breaking the Limits of Open-Weight CLIP: An Optimization Framework for Self-supervised Fine-tuning of CLIP. CoRR Anant Mehta, Xiyuan Wei, Xingyu Chen, Tianbao Yang
2026	A*	`conf`	CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation. AAAI Chenchen Kuai, Chenhao Wu, Yang Zhou, Xiubin Bruce Wang, Tianbao Yang, Zhengzhong Tu, Zihao Li, Yunlong Zhang
2026	J	`jnl`	DRTriton: Large-Scale Synthetic Data Reinforcement Learning for Triton Kernel Generation. CoRR Siqi Guo, Ming Lin, Tianbao Yang
2026	J	`jnl`	Optimizing Unnormalized Statistical Models Through Compositional Optimization. IEEE Trans. Pattern Anal. Mach. Intell. Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
2026	J	`jnl`	Single-loop Algorithms for Stochastic Non-Convex Optimization with Weakly-Convex Constraints. Trans. Mach. Learn. Res. Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang
2025	A*	`conf`	A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization. ICML Bokun Wang, Tianbao Yang
2025	—	`conf`	AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-Rays. MICCAI (6) Chenlang Yi, Zizhan Xiong, Qi Qi, Xiyuan Wei, Girish Bathla, Ching-Long Lin, Bobak J. Mortazavi, Tianbao Yang
2025	J	`jnl`	AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays. CoRR Chenlang Yi, Zizhan Xiong, Qi Qi, Xiyuan Wei, Girish Bathla, Ching-Long Lin, Bobak Jack Mortazavi, Tianbao Yang
2025	J	`jnl`	AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving. Trans. Mach. Learn. Res. Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, Yang Zhou, Huaxiu Yao, Zhengzhong Tu
2025	J	`jnl`	CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation. CoRR Chenchen Kuai, Chenhao Wu, Yang Zhou, Xiubin Bruce Wang, Tianbao Yang, Zhengzhong Tu, Zihao Li, Yunlong Zhang
2025	J	`jnl`	DRPO: Efficient Reasoning via Decoupled Reward Policy Optimization. CoRR Gang Li, Yan Chen, Ming Lin, Tianbao Yang
2025	J	`jnl`	DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization. CoRR Gang Li, Ming Lin, Tomer Galanti, Zhengzhong Tu, Tianbao Yang
2025	A*	`conf`	Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning. ICML Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang
2025	J	`jnl`	Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning. CoRR Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang
2025	A*	`conf`	Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data. ICML Siqi Guo, Ilgee Hong, Vicente Balmaseda, Changlong Yu, Liang Qiu, Xin Liu, Haoming Jiang, Tuo Zhao, Tianbao Yang
2025	J	`jnl`	Discriminative Finetuning of Generative Large Language Models without Reward Models and Preference Data. CoRR Siqi Guo, Ilgee Hong, Vicente Balmaseda, Tuo Zhao, Tianbao Yang
2025	J	`jnl`	Few-Shot Defect Recognition for New Energy Equipment via Multimodal Harmony. IEEE Internet Things J. Zhenwei Wang, Pengfei Wang, Mohammad S. Obaidat, Zhonghao Zhang, Tianbao Yang, Jintao Zheng, Jianxin Zhang, Qiang Zhang
2025	A*	`conf`	Gradient Aligned Regression via Pairwise Losses. ICML Dixian Zhu, Tianbao Yang, Livnat Jerby
2025	J	`jnl`	Learning to Rank with Top- CoRR Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Learning to Rank with Top-$K$ Fairness. Trans. Mach. Learn. Res. Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang
2025	A*	`conf`	Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws. ICML Xiyuan Wei, Ming Lin, Fanjiang Ye, Fengguang Song, Liangliang Cao, My T. Thai, Tianbao Yang
2025	J	`jnl`	Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws. CoRR Xiyuan Wei, Ming Lin, Fanjiang Ye, Fengguang Song, Liangliang Cao, My T. Thai, Tianbao Yang
2025	J	`jnl`	Multi-Output Distributional Fairness via Post-Processing. Trans. Mach. Learn. Res. Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang
2025	J	`jnl`	Myna: Masking-Based Contrastive Learning of Musical Representations. CoRR Ori Yonay, Tracy Hammond, Tianbao Yang
2025	J	`jnl`	NeuCLIP: Efficient Large-Scale CLIP Training with Neural Normalizer Optimization. CoRR Xiyuan Wei, Chih-Jen Lin, Tianbao Yang
2025	A*	`conf`	On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. ICLR Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang
2025	J	`jnl`	Optimal large-scale stochastic optimization of NDCG surrogates for deep learning. Mach. Learn. Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Wei-Wei Tu, Lijun Zhang, Tianbao Yang
2025	J	`jnl`	Revisiting Stochastic Multi-Level Compositional Optimization. IEEE Trans. Pattern Anal. Mach. Intell. Wei Jiang, Sifan Yang, Yibo Wang, Tianbao Yang, Lijun Zhang
2025	J	`jnl`	Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning. CoRR Achleshwar Luthra, Tianbao Yang, Tomer Galanti
2025	J	`jnl`	Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen. CoRR Zihao Li, Xinyuan Cao, Xiangbo Gao, Kexin Tian, Keshu Wu, Mohammad Anis, Hao Zhang, Keke Long, Jiwan Jiang, Xiaopeng Li, Yunlong Zhang, Tianbao Yang, Dominique Lord, Zhengzhong Tu, Yang Zhou
2025	J	`jnl`	Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints. CoRR Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization. CoRR Xingyu Chen, Bokun Wang, Min Yang, Quanqi Hu, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Stochastic Primal-Dual Double Block-Coordinate for Two- way Partial AUC Maximization. Trans. Mach. Learn. Res. Linli Zhou, Bokun Wang, My T. Thai, Tianbao Yang
2025	J	`jnl`	Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization. CoRR Linli Zhou, Bokun Wang, My T. Thai, Tianbao Yang
2025	J	`jnl`	Unified convergence analysis for adaptive optimization with moving average estimator. Mach. Learn. Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2025	J	`jnl`	Universal Online Convex Optimization Meets Second-order Bounds. J. Mach. Learn. Res. Lijun Zhang, Yibo Wang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
2024	—	`conf`	A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability. BHI Ryan King, Conrad Krueger, Ethan Veselka, Tianbao Yang, Bobak J. Mortazavi
2024	A*	`conf`	Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. NeurIPS Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao
2024	J	`jnl`	Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. CoRR Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao
2024	—	`conf`	An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data. BHI Ryan King, Shivesh Kodali, Conrad Krueger, Tianbao Yang, Bobak J. Mortazavi
2024	J	`jnl`	An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data. CoRR Ryan King, Shivesh Kodali, Conrad Krueger, Tianbao Yang, Bobak J. Mortazavi
2024	J	`jnl`	AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving. CoRR Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, Yang Zhou, Huaxiu Yao, Zhengzhong Tu
2024	A*	`conf`	Communication-Efficient Federated Group Distributionally Robust Optimization. NeurIPS Zhishuai Guo, Tianbao Yang
2024	J	`jnl`	Communication-Efficient Federated Group Distributionally Robust Optimization. CoRR Zhishuai Guo, Tianbao Yang
2024	A*	`conf`	Efficient and Effective Implicit Dynamic Graph Neural Network. KDD Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari
2024	J	`jnl`	Efficient and Effective Implicit Dynamic Graph Neural Network. CoRR Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari
2024	—	`conf`	Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. WWW (Companion Volume) Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee
2024	J	`jnl`	FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources. CoRR Xiyuan Wei, Fanjiang Ye, Ori Yonay, Xingyu Chen, Baixi Sun, Dingwen Tao, Tianbao Yang
2024	J	`jnl`	Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models. CoRR Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang
2024	J	`jnl`	Multi-Output Distributional Fairness via Post-Processing. CoRR Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang
2024	J	`jnl`	On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. CoRR Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang
2024	J	`jnl`	Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang
2024	A*	`conf`	Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. NeurIPS Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang
2024	J	`jnl`	Single-loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. CoRR Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang
2024	A*	`conf`	Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. ICML Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
2024	A*	`conf`	To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. ICML Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang
2024	J	`jnl`	To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. CoRR Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang
2024	J	`jnl`	Which LLMs are Difficult to Detect? A Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection. CoRR Shantanu Thorat, Tianbao Yang
2023	J	`jnl`	ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization. CoRR Bokun Wang, Tianbao Yang
2023	J	`jnl`	AUC Maximization in the Era of Big Data and AI: A Survey. ACM Comput. Surv. Tianbao Yang, Yiming Ying
2023	J	`jnl`	AUC-mixup: Deep AUC Maximization with Mixup. CoRR Jianzhi Xv, Gang Li, Tianbao Yang
2023	J	`jnl`	Attentional-Biased Stochastic Gradient Descent. Trans. Mach. Learn. Res. Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2023	A*	`conf`	Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. ICML Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
2023	J	`jnl`	Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. CoRR Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
2023	J	`jnl`	Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. CoRR Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee
2023	J	`jnl`	Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. J. Mach. Learn. Res. Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
2023	A*	`conf`	FeDXL: Provable Federated Learning for Deep X-Risk Optimization. ICML Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
2023	A*	`conf`	Federated Compositional Deep AUC Maximization. NeurIPS Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
2023	J	`jnl`	Federated Compositional Deep AUC Maximization. CoRR Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
2023	A*	`conf`	Generalization Analysis for Contrastive Representation Learning. ICML Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
2023	J	`jnl`	Generalization Analysis for Contrastive Representation Learning. CoRR Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
2023	A*	`conf`	Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. ICML Dixian Zhu, Yiming Ying, Tianbao Yang
2023	A*	`conf`	Learning Unnormalized Statistical Models via Compositional Optimization. ICML Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
2023	J	`jnl`	Learning Unnormalized Statistical Models via Compositional Optimization. CoRR Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
2023	A*	`conf`	LibAUC: A Deep Learning Library for X-Risk Optimization. KDD Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang
2023	J	`jnl`	LibAUC: A Deep Learning Library for X-Risk Optimization. CoRR Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang
2023	A*	`conf`	Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness. NeurIPS Gang Li, Wei Tong, Tianbao Yang
2023	J	`jnl`	Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. J. Mach. Learn. Res. Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2023	—	`conf`	Multimodal Pretraining of Medical Time Series and Notes. ML4H@NeurIPS Ryan King, Tianbao Yang, Bobak J. Mortazavi
2023	J	`jnl`	Multimodal Pretraining of Medical Time Series and Notes. CoRR Ryan King, Tianbao Yang, Bobak Mortazavi
2023	A*	`conf`	Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. NeurIPS Quanqi Hu, Dixian Zhu, Tianbao Yang
2023	J	`jnl`	Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. CoRR Quanqi Hu, Dixian Zhu, Tianbao Yang
2023	A*	`conf`	Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. ICML Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang
2023	J	`jnl`	Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. CoRR Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang
2023	A*	`conf`	Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. ICML Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
2023	J	`jnl`	Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. CoRR Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
2023	A*	`conf`	SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. NeurIPS Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang
2023	J	`jnl`	SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. CoRR Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang
2023	J	`jnl`	Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. CoRR Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
2023	A*	`conf`	Stochastic Approximation Approaches to Group Distributionally Robust Optimization. NeurIPS Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou
2023	J	`jnl`	Stochastic Approximation Approaches to Group Distributionally Robust Optimization. CoRR Lijun Zhang, Peng Zhao, Tianbao Yang, Zhi-Hua Zhou
2023	J	`jnl`	Stochastic Constrained DRO with a Complexity Independent of Sample Size. Trans. Mach. Learn. Res. Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang
2023	A	`conf`	Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS Yao Yao, Qihang Lin, Tianbao Yang
2022	A*	`conf`	A Simple yet Universal Strategy for Online Convex Optimization. ICML Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
2022	J	`jnl`	AUC Maximization in the Era of Big Data and AI: A Survey. CoRR Tianbao Yang, Yiming Ying
2022	J	`jnl`	Advanced graph and sequence neural networks for molecular property prediction and drug discovery. Bioinform. Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji
2022	J	`jnl`	Algorithmic Foundation of Deep X-Risk Optimization. CoRR Tianbao Yang
2022	J	`jnl`	Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices. CoRR Dixian Zhu, Xiaodong Wu, Tianbao Yang
2022	A*	`conf`	Compositional Training for End-to-End Deep AUC Maximization. ICLR Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang
2022	J	`jnl`	Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang
2022	J	`jnl`	FedX: Federated Learning for Compositional Pairwise Risk Optimization. CoRR Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
2022	J	`jnl`	Finite-Sum Compositional Stochastic Optimization: Theory and Applications. CoRR Bokun Wang, Tianbao Yang
2022	A*	`conf`	Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. ICML Bokun Wang, Tianbao Yang
2022	A*	`conf`	GraphFM: Improving Large-Scale GNN Training via Feature Momentum. ICML Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
2022	J	`jnl`	GraphFM: Improving Large-Scale GNN Training via Feature Momentum. CoRR Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
2022	A*	`conf`	Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS Yao Yao, Qihang Lin, Tianbao Yang
2022	J	`jnl`	Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR Yao Yao, Qihang Lin, Tianbao Yang
2022	A*	`conf`	Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. ICML Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang
2022	J	`jnl`	Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. CoRR Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang
2022	A	`conf`	Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. AISTATS Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang
2022	A*	`conf`	Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. NeurIPS Quanqi Hu, Yongjian Zhong, Tianbao Yang
2022	J	`jnl`	Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. CoRR Quanqi Hu, Yongjian Zhong, Tianbao Yang
2022	A*	`conf`	Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. NeurIPS Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang
2022	J	`jnl`	Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. CoRR Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang
2022	A*	`conf`	Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. ICML Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
2022	J	`jnl`	Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. CoRR Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
2022	A*	`conf`	Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. ICML Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang
2022	J	`jnl`	Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. CoRR Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang
2022	J	`jnl`	SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification. Remote. Sens. Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, Hao Sun, Tianbao Yang, Changwei Tan
2022	A*	`conf`	Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. NeurIPS Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang
2022	J	`jnl`	Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. CoRR Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang
2022	J	`jnl`	Stochastic Constrained DRO with a Complexity Independent of Sample Size. CoRR Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang
2022	J	`jnl`	Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR Yao Yao, Qihang Lin, Tianbao Yang
2022	J	`jnl`	Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2022	A*	`conf`	When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. ICML Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang
2022	J	`jnl`	When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. CoRR Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang
2021	J	`jnl`	A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2021	J	`jnl`	A Simple yet Universal Strategy for Online Convex Optimization. CoRR Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
2021	J	`jnl`	A Unified DRO View of Multi-class Loss Functions with top-N Consistency. CoRR Dixian Zhu, Tianbao Yang
2021	A*	`conf`	An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
2021	J	`jnl`	Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities. CoRR Tianbao Yang
2021	A*	`conf`	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
2021	J	`jnl`	Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
2021	J	`jnl`	First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
2021	J	`jnl`	Hybrid safe-strong rules for efficient optimization in lasso-type problems. Comput. Stat. Data Anal. Yaohui Zeng, Tianbao Yang, Patrick Breheny
2021	A*	`conf`	Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. ICCV Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
2021	J	`jnl`	Memory-based Optimization Methods for Model-Agnostic Meta-Learning. CoRR Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2021	J	`jnl`	Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. CoRR Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang
2021	J	`jnl`	On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2021	A*	`conf`	Online Convex Optimization with Continuous Switching Constraint. NeurIPS Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
2021	J	`jnl`	Online Convex Optimization with Continuous Switching Constraint. CoRR Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
2021	J	`jnl`	Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization. CoRR Zhishuai Guo, Tianbao Yang
2021	A*	`conf`	Revisiting Smoothed Online Learning. NeurIPS Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
2021	J	`jnl`	Revisiting Smoothed Online Learning. CoRR Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
2021	A*	`conf`	Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. NeurIPS Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
2021	J	`jnl`	Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. CoRR Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
2021	A*	`conf`	Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. ICML Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
2021	J	`jnl`	Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. CoRR Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
2021	J	`jnl`	Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. CoRR Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
2021	A*	`conf`	Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. NeurIPS Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
2020	J	`jnl`	A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. J. Mach. Learn. Res. Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
2020	A*	`conf`	A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
2020	J	`jnl`	A Practical Online Method for Distributionally Deep Robust Optimization. CoRR Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
2020	—	`conf`	A Simple and Effective Framework for Pairwise Deep Metric Learning. ECCV (27) Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang
2020	—	`conf`	Accelerating Deep Learning with Millions of Classes. ECCV (23) Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
2020	J	`jnl`	Adam CoRR Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang
2020	A*	`conf`	Adversarial Localized Energy Network for Structured Prediction. AAAI Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang
2020	J	`jnl`	Attentional Biased Stochastic Gradient for Imbalanced Classification. CoRR Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang
2020	A*	`conf`	Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
2020	J	`jnl`	Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
2020	A*	`conf`	Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval. AAAI Dixian Zhu, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Wei Cheng, Bo Zong, Jingchao Ni, Takehiko Mizoguchi, Tianbao Yang, Haifeng Chen
2020	J	`jnl`	Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems. CoRR Zhishuai Guo, Zhuoning Yuan, Yan Yan, Tianbao Yang
2020	J	`jnl`	High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. Mach. Learn. Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin
2020	J	`jnl`	Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent. J. Parallel Distributed Comput. Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi
2020	A*	`conf`	Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
2020	A	`conf`	Minimizing Dynamic Regret and Adaptive Regret Simultaneously. AISTATS Lijun Zhang, Shiyin Lu, Tianbao Yang
2020	J	`jnl`	Minimizing Dynamic Regret and Adaptive Regret Simultaneously. CoRR Lijun Zhang, Shiyin Lu, Tianbao Yang
2020	J	`jnl`	Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise. CoRR Yan Yan, Xin Man, Tianbao Yang
2020	A*	`conf`	Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	A*	`conf`	Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML Runchao Ma, Qihang Lin, Tianbao Yang
2020	J	`jnl`	Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives. CoRR Zhishuai Guo, Zixuan Wu, Yan Yan, Xiaoyu Wang, Tianbao Yang
2020	J	`jnl`	Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. CoRR Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
2020	J	`jnl`	Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	A*	`conf`	Stochastic AUC Maximization with Deep Neural Networks. ICLR Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2020	A*	`conf`	Stochastic Optimization for Non-convex Inf-Projection Problems. ICML Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang
2020	A*	`conf`	Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
2020	J	`jnl`	Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu
2019	J	`jnl`	A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
2019	A	`conf`	A Robust Zero-Sum Game Framework for Pool-based Active Learning. AISTATS Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang
2019	J	`jnl`	A simple homotopy proximal mapping algorithm for compressive sensing. Mach. Learn. Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
2019	J	`jnl`	Advancing non-convex and constrained learning: challenges and opportunities. AI Matters Tianbao Yang
2019	J	`jnl`	Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das
2019	A*	`conf`	EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch. CVPR Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran
2019	A*	`conf`	Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang
2019	J	`jnl`	Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
2019	A	`conf`	Learning with Non-Convex Truncated Losses by SGD. UAI Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
2019	A*	`conf`	Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS Yi Xu, Rong Jin, Tianbao Yang
2019	A*	`conf`	On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang
2019	J	`jnl`	Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion. J. Mach. Learn. Res. Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2019	A*	`conf`	Stagewise Training Accelerates Convergence of Testing Error Over SGD. NeurIPS Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang
2019	J	`jnl`	Stochastic AUC Maximization with Deep Neural Networks. CoRR Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
2019	A*	`conf`	Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang
2019	J	`jnl`	Stochastic Optimization for Non-convex Inf-Projection Problems. CoRR Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang
2019	J	`jnl`	Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
2019	J	`jnl`	Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. CoRR Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
2019	—	`conf`	Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions. ICLR (Poster) Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang
2019	J	`jnl`	a simple and effective framework for pairwise deep metric learning. CoRR Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang
2018	A*	`conf`	A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization. IJCAI Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang
2018	J	`jnl`	A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data Cogn. Comput. Dixian Zhu, Changjie Cai, Tianbao Yang, Xun Zhou
2018	A	`conf`	A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. AISTATS Tianbao Yang, Zhe Li, Lijun Zhang
2018	A*	`conf`	A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
2018	J	`jnl`	A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
2018	A*	`conf`	Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
2018	J	`jnl`	An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints. CoRR Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang
2018	—	`conf`	An Indirect-Direct event-triggered mechanism for networked control system against DoS attacks. ANZCC Zihao Cheng, Dong Yue, Songlin Hu, Tianbao Yang, Lei Chen
2018	—	`ch.`	Combining Link and Content for Community Detection. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
2018	A*	`conf`	Dynamic Regret of Strongly Adaptive Methods. ICML Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2018	J	`jnl`	EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching. CoRR Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran
2018	A*	`conf`	Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
2018	J	`jnl`	Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
2018	A*	`conf`	Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang
2018	A*	`conf`	Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
2018	A*	`conf`	First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS Yi Xu, Rong Jin, Tianbao Yang
2018	A*	`conf`	Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data. KDD Zhuoning Yuan, Xun Zhou, Tianbao Yang
2018	—	`conf`	How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. ECCV (8) Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong
2018	J	`jnl`	How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. CoRR Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong
2018	—	`conf`	Improving Sequential Determinantal Point Processes for Supervised Video Summarization. ECCV (3) Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong
2018	J	`jnl`	Improving Sequential Determinantal Point Processes for Supervised Video Summarization. CoRR Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong
2018	J	`jnl`	Learning Discriminators as Energy Networks in Adversarial Learning. CoRR Pingbo Pan, Yan Yan, Tianbao Yang, Yi Yang
2018	J	`jnl`	Learning with Non-Convex Truncated Losses by SGD. CoRR Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
2018	A*	`conf`	Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML Qihang Lin, Runchao Ma, Tianbao Yang
2018	J	`jnl`	Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2018	J	`jnl`	RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. Tianbao Yang, Qihang Lin
2018	A*	`conf`	SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. ICML Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang
2018	J	`jnl`	Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD? CoRR Tianbao Yang, Yan Yan, Zhuoning Yuan, Rong Jin
2017	A*	`conf`	A Framework of Online Learning with Imbalanced Streaming Data. AAAI Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen
2017	—	`ch.`	A Multisource Domain Generalization Approach to Visual Attribute Detection. Domain Adaptation in Computer Vision Applications Chuang Gan, Tianbao Yang, Boqing Gong
2017	A*	`conf`	A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML Tianbao Yang, Qihang Lin, Lijun Zhang
2017	J	`jnl`	A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. CoRR Tianbao Yang, Zhe Li, Lijun Zhang
2017	A*	`conf`	A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis. AAAI Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin
2017	—	`conf`	ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
2017	—	`conf`	Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS Mingrui Liu, Tianbao Yang
2017	—	`conf`	Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS Yi Xu, Qihang Lin, Tianbao Yang
2017	J	`jnl`	Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. J. Mach. Learn. Res. Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
2017	A*	`conf`	Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang
2017	A*	`conf`	Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds. COLT Lijun Zhang, Tianbao Yang, Rong Jin
2017	J	`jnl`	Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n CoRR Lijun Zhang, Tianbao Yang, Rong Jin
2017	—	`conf`	Improved Dynamic Regret for Non-degenerate Functions. NIPS Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
2017	J	`jnl`	SEP-Nets: Small and Effective Pattern Networks. CoRR Zhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang
2017	A*	`conf`	SVD-free Convex-Concave Approaches for Nuclear Norm Regularization. IJCAI Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang
2017	A*	`conf`	Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML Yi Xu, Qihang Lin, Tianbao Yang
2017	J	`jnl`	Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR Mingrui Liu, Tianbao Yang
2017	J	`jnl`	Strongly Adaptive Regret Implies Optimally Dynamic Regret. CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2016	J	`jnl`	Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR Yi Xu, Qihang Lin, Tianbao Yang
2016	J	`jnl`	Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. CoRR Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang
2016	A*	`conf`	Fast and Accurate Refined Nyström-Based Kernel SVM. AAAI Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin
2016	—	`conf`	Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
2016	J	`jnl`	Hybrid-DCA: A Double Asynchronous Approach for Stochastic Dual Coordinate Ascent. CoRR Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi
2016	—	`conf`	Improved Dropout for Shallow and Deep Learning. NIPS Zhe Li, Boqing Gong, Tianbao Yang
2016	J	`jnl`	Improved Dropout for Shallow and Deep Learning. CoRR Zhe Li, Boqing Gong, Tianbao Yang
2016	J	`jnl`	Improved dynamic regret for non-degeneracy functions. CoRR Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
2016	A*	`conf`	Learning Attributes Equals Multi-Source Domain Generalization. CVPR Chuang Gan, Tianbao Yang, Boqing Gong
2016	J	`jnl`	Learning Attributes Equals Multi-Source Domain Generalization. CoRR Chuang Gan, Tianbao Yang, Boqing Gong
2016	J	`jnl`	On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
2016	A*	`conf`	Online Asymmetric Active Learning with Imbalanced Data. KDD Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan
2016	A*	`conf`	Online Stochastic Linear Optimization under One-bit Feedback. ICML Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou
2016	A	`conf`	Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang
2016	B	`conf`	Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach. ALT Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2016	A*	`conf`	Stochastic Optimization for Kernel PCA. AAAI Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
2016	A*	`conf`	Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. ICML Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi
2016	J	`jnl`	Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. CoRR Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi
2015	A	`conf`	A Simple Homotopy Algorithm for Compressive Sensing. AISTATS Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	A*	`conf`	An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints. KDD Jinfeng Yi, Lijun Zhang, Tianbao Yang, Wei Liu, Jun Wang
2015	A*	`conf`	An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection. ICML Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
2015	J	`jnl`	An efficient primal dual prox method for non-smooth optimization. Mach. Learn. Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
2015	J	`jnl`	Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion. CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	A*	`conf`	Big Data Analytics: Optimization and Randomization. KDD Tianbao Yang, Qihang Lin, Rong Jin
2015	J	`jnl`	Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR Adams Wei Yu, Qihang Lin, Tianbao Yang
2015	J	`jnl`	Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin
2015	A*	`conf`	Hyper-class augmented and regularized deep learning for fine-grained image classification. CVPR Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin
2015	—	`conf`	In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones. ICHI Syed Shabih Hasan, Ryan Brummet, Octav Chipara, Yu-Hsiang Wu, Tianbao Yang
2015	A*	`conf`	Online Bandit Learning for a Special Class of Non-Convex Losses. AAAI Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	J	`jnl`	Online Stochastic Linear Optimization under One-bit Feedback. CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	J	`jnl`	Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach. CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	J	`jnl`	Stochastic Proximal Gradient Descent for Nuclear Norm Regularization. CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
2015	A*	`conf`	Theory of Dual-sparse Regularized Randomized Reduction. ICML Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
2015	J	`jnl`	Theory of Dual-sparse Regularized Randomized Reduction. CoRR Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
2014	J	`jnl`	A Simple Homotopy Proximal Mapping for Compressive Sensing. CoRR Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
2014	—	`ch.`	Combining Link and Content for Community Detection. Encyclopedia of Social Network Analysis and Mining Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
2014	A	`conf`	Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs. AISTATS Jianhui Chen, Tianbao Yang, Shenghuo Zhu
2014	—	`conf`	Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities. NIPS Tianbao Yang, Rong Jin
2014	J	`jnl`	Object-centric Sampling for Fine-grained Image Classification. CoRR Xiaoyu Wang, Tianbao Yang, Guobin Chen, Yuanqing Lin
2014	J	`jnl`	On Data Preconditioning for Regularized Loss Minimization. CoRR Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
2014	J	`jnl`	Random Projections for Classification: A Recovery Approach. IEEE Trans. Inf. Theory Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu
2014	J	`jnl`	Regret bounded by gradual variation for online convex optimization. Mach. Learn. Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
2013	—	`conf`	A Directed Inference Approach towards Multi-class Multi-model Fusion. MCS Tianbao Yang, Lei Wu, Piero P. Bonissone
2013	J	`jnl`	A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent CoRR Rong Jin, Tianbao Yang, Shenghuo Zhu
2013	B	`conf`	Community detection by popularity based models for authored networked data. ASONAM Tianbao Yang, Prakash Mandayam Comar, Linli Xu
2013	J	`jnl`	Efficient Stochastic Gradient Descent for Strongly Convex Optimization CoRR Tianbao Yang, Lijun Zhang
2013	J	`jnl`	Improved Bounds for the Nyström Method With Application to Kernel Classification. IEEE Trans. Inf. Theory Rong Jin, Tianbao Yang, Mehrdad Mahdavi, Yufeng Li, Zhi-Hua Zhou
2013	J	`jnl`	O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions CoRR Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He
2013	—	`conf`	O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions. ICML (3) Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He
2013	J	`jnl`	On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent. CoRR Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin
2013	J	`jnl`	Online Multiple Kernel Classification. Mach. Learn. Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang
2013	A*	`conf`	Recovering the Optimal Solution by Dual Random Projection. COLT Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu
2013	J	`jnl`	Sparse Multiple Kernel Learning with Geometric Convergence Rate CoRR Rong Jin, Tianbao Yang, Mehrdad Mahdavi
2013	—	`conf`	Stochastic Convex Optimization with Multiple Objectives. NIPS Mehrdad Mahdavi, Tianbao Yang, Rong Jin
2013	—	`conf`	Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent. NIPS Tianbao Yang
2012	J	`jnl`	A Bayesian Framework for Community Detection Integrating Content and Link CoRR Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
2012	A*	`conf`	A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound. ICML Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han
2012	J	`jnl`	An Efficient Primal-Dual Prox Method for Non-Smooth Optimization CoRR Tianbao Yang, Rong Jin, Mehrdad Mahdavi, Shenghuo Zhu
2012	J	`jnl`	An Improved Bound for the Nystrom Method for Large Eigengap CoRR Mehrdad Mahdavi, Tianbao Yang, Rong Jin
2012	J	`jnl`	Efficient Constrained Regret Minimization CoRR Mehrdad Mahdavi, Tianbao Yang, Rong Jin
2012	J	`jnl`	Influence Analysis in the Blogosphere CoRR Michinari Momma, Yun Chi, Yuanqing Lin, Shenghuo Zhu, Tianbao Yang
2012	—	`conf`	Learning kernel combination from noisy pairwise constraints. SSP Tianbao Yang, Rong Jin, Anil K. Jain
2012	J	`jnl`	Multiple Kernel Learning from Noisy Labels by Stochastic Programming CoRR Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou
2012	A*	`conf`	Multiple Kernel Learning from Noisy Labels by Stochastic Programming. ICML Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou
2012	—	`conf`	Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison. NIPS Tianbao Yang, Yufeng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou
2012	A*	`conf`	Online Kernel Selection: Algorithms and Evaluations. AAAI Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
2012	A*	`conf`	Online Optimization with Gradual Variations. COLT Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu
2012	J	`jnl`	Online Stochastic Optimization with Multiple Objectives CoRR Mehrdad Mahdavi, Tianbao Yang, Rong Jin
2012	J	`jnl`	Recovering Optimal Solution by Dual Random Projection CoRR Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang
2012	A*	`conf`	Robust Ensemble Clustering by Matrix Completion. ICDM Jinfeng Yi, Tianbao Yang, Rong Jin, Anil K. Jain, Mehrdad Mahdavi
2012	—	`conf`	Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning. NIPS Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain, Tianbao Yang
2012	—	`conf`	Stochastic Gradient Descent with Only One Projection. NIPS Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi
2012	J	`jnl`	Trading regret for efficiency: online convex optimization with long term constraints. J. Mach. Learn. Res. Mehrdad Mahdavi, Rong Jin, Tianbao Yang
2011	B	`conf`	A kernel density based approach for large scale image retrieval. ICMR Wei Tong, Fengjie Li, Tianbao Yang, Rong Jin, Anil K. Jain
2011	J	`jnl`	Detecting communities and their evolutions in dynamic social networks - a Bayesian approach. Mach. Learn. Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin
2011	J	`jnl`	Improved Bound for the Nystrom's Method and its Application to Kernel Classification CoRR Rong Jin, Tianbao Yang, Mehrdad Mahdavi
2011	A*	`conf`	Online AUC Maximization. ICML Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang
2011	J	`jnl`	Regret Bound by Variation for Online Convex Optimization CoRR Tianbao Yang, Rong Jin, Mehrdad Mahdavi
2011	J	`jnl`	Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints CoRR Mehrdad Mahdavi, Rong Jin, Tianbao Yang
2010	A	`conf`	Directed Network Community Detection: A Popularity and Productivity Link Model. SDM Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin
2010	A*	`conf`	Learning from Noisy Side Information by Generalized Maximum Entropy Model. ICML Tianbao Yang, Rong Jin, Anil K. Jain
2010	B	`conf`	Online Multiple Kernel Learning: Algorithms and Mistake Bounds. ALT Rong Jin, Steven C. H. Hoi, Tianbao Yang
2010	A*	`conf`	Unsupervised transfer classification: application to text categorization. KDD Tianbao Yang, Rong Jin, Anil K. Jain, Yang Zhou, Wei Tong
2009	A	`conf`	A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks. SDM Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin
2009	A	`conf`	A Bayesian Framework for Community Detection Integrating Content and Link. UAI Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
2009	A*	`conf`	Combining link and content for community detection: a discriminative approach. KDD Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu