| 2026 | AAAI | CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation. | Chenchen Kuai, Chenhao Wu, Yang Zhou, Xiubin Bruce Wang, Tianbao Yang, Zhengzhong Tu, Zihao Li, Yunlong Zhang |
| 2025 | ICLR | On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. | Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang |
| 2025 | ICML | Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data. | Siqi Guo, Ilgee Hong, Vicente Balmaseda, Changlong Yu, Liang Qiu, Xin Liu, Haoming Jiang, Tuo Zhao, Tianbao Yang |
| 2025 | ICML | Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning. | Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang |
| 2025 | ICML | A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization. | Bokun Wang, Tianbao Yang |
| 2025 | ICML | Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws. | Xiyuan Wei, Ming Lin, Fanjiang Ye, Fengguang Song, Liangliang Cao, My T. Thai, Tianbao Yang |
| 2025 | ICML | Gradient Aligned Regression via Pairwise Losses. | Dixian Zhu, Tianbao Yang, Livnat Jerby |
| 2025 | MICCAI | AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-Rays. | Chenlang Yi, Zizhan Xiong, Qi Qi, Xiyuan Wei, Girish Bathla, Ching-Long Lin, Bobak J. Mortazavi, Tianbao Yang |
| 2024 | ICML | To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. | Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang |
| 2024 | ICML | Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. | Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying |
| 2024 | KDD | Efficient and Effective Implicit Dynamic Graph Neural Network. | Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari |
| 2024 | WWW | Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. | Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee |
| 2023 | AISTATS | Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. | Yao Yao, Qihang Lin, Tianbao Yang |
| 2023 | ICML | FeDXL: Provable Federated Learning for Deep X-Risk Optimization. | Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang |
| 2023 | ICML | Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. | Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang |
| 2023 | ICML | Learning Unnormalized Statistical Models via Compositional Optimization. | Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang |
| 2023 | ICML | Generalization Analysis for Contrastive Representation Learning. | Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou |
| 2023 | ICML | 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, Tianbao Yang |
| 2023 | ICML | Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. | Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang |
| 2023 | ICML | Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. | Dixian Zhu, Yiming Ying, Tianbao Yang |
| 2023 | KDD | LibAUC: A Deep Learning Library for X-Risk Optimization. | Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang |
| 2022 | AISTATS | Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. | Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang |
| 2022 | ICLR | Compositional Training for End-to-End Deep AUC Maximization. | Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang |
| 2022 | ICML | Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. | Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang |
| 2022 | ICML | Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. | Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang |
| 2022 | ICML | Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. | Bokun Wang, Tianbao Yang |
| 2022 | ICML | Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. | Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang |
| 2022 | ICML | GraphFM: Improving Large-Scale GNN Training via Feature Momentum. | Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji |
| 2022 | ICML | A Simple yet Universal Strategy for Online Convex Optimization. | Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang |
| 2022 | ICML | When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. | Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang |
| 2021 | ICCV | Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. | Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang |
| 2021 | ICML | Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. | Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying |
| 2021 | ICML | Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. | Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang |
| 2020 | AAAI | Adversarial Localized Energy Network for Structured Prediction. | Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang |
| 2020 | AAAI | Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval. | Dixian Zhu, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Wei Cheng, Bo Zong, Jingchao Ni, Takehiko Mizoguchi, Tianbao Yang, Haifeng Chen |
| 2020 | AISTATS | Minimizing Dynamic Regret and Adaptive Regret Simultaneously. | Lijun Zhang, Shiyin Lu, Tianbao Yang |
| 2020 | ECCV | A Simple and Effective Framework for Pairwise Deep Metric Learning. | Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang |
| 2020 | ECCV | Accelerating Deep Learning with Millions of Classes. | Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang |
| 2020 | ICLR | Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. | Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang |
| 2020 | ICLR | Stochastic AUC Maximization with Deep Neural Networks. | Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang |
| 2020 | ICML | Stochastic Optimization for Non-convex Inf-Projection Problems. | Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang |
| 2020 | ICML | Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. | Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang |
| 2020 | ICML | Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. | Runchao Ma, Qihang Lin, Tianbao Yang |
| 2019 | AISTATS | A Robust Zero-Sum Game Framework for Pool-based Active Learning. | Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang |
| 2019 | CVPR | EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch. | Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran |
| 2019 | ICLR | Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions. | Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang |
| 2019 | ICML | Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. | Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang |
| 2019 | ICML | Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. | Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang |
| 2019 | IJCAI | On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. | Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang |
| 2019 | UAI | Learning with Non-Convex Truncated Losses by SGD. | Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang |
| 2018 | AISTATS | A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. | Tianbao Yang, Zhe Li, Lijun Zhang |
| 2018 | ECCV | How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. | Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong |
| 2018 | ECCV | Improving Sequential Determinantal Point Processes for Supervised Video Summarization. | Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong |
| 2018 | ICML | SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. | Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang |
| 2018 | ICML | Level-Set Methods for Finite-Sum Constrained Convex Optimization. | Qihang Lin, Runchao Ma, Tianbao Yang |
| 2018 | ICML | Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. | Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang |
| 2018 | ICML | Dynamic Regret of Strongly Adaptive Methods. | Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou |
| 2018 | IJCAI | A Unified Analysis of Stochastic Momentum Methods for Deep Learning. | Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang |
| 2018 | IJCAI | A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization. | Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang |
| 2018 | KDD | Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data. | Zhuoning Yuan, Xun Zhou, Tianbao Yang |
| 2017 | AAAI | A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis. | Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin |
| 2017 | AAAI | Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. | Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang |
| 2017 | AAAI | A Framework of Online Learning with Imbalanced Streaming Data. | Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen |
| 2017 | COLT | Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds. | Lijun Zhang, Tianbao Yang, Rong Jin |
| 2017 | ICML | Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. | Yi Xu, Qihang Lin, Tianbao Yang |
| 2017 | ICML | A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. | Tianbao Yang, Qihang Lin, Lijun Zhang |
| 2017 | IJCAI | SVD-free Convex-Concave Approaches for Nuclear Norm Regularization. | Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang |
| 2016 | AAAI | Stochastic Optimization for Kernel PCA. | Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou |
| 2016 | AAAI | Fast and Accurate Refined Nystrm-Based Kernel SVM. | Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin |
| 2016 | ALT | Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach. | Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou |
| 2016 | CVPR | Learning Attributes Equals Multi-Source Domain Generalization. | Chuang Gan, Tianbao Yang, Boqing Gong |
| 2016 | ICML | Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. | Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi |
| 2016 | ICML | Online Stochastic Linear Optimization under One-bit Feedback. | Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou |
| 2016 | KDD | Online Asymmetric Active Learning with Imbalanced Data. | Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan |
| 2016 | UAI | Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. | Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang |
| 2015 | AAAI | Online Bandit Learning for a Special Class of Non-Convex Losses. | Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou |
| 2015 | AISTATS | A Simple Homotopy Algorithm for Compressive Sensing. | Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou |
| 2015 | CVPR | Hyper-class augmented and regularized deep learning for fine-grained image classification. | Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin |
| 2015 | ICML | An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection. | Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu |
| 2015 | ICML | Theory of Dual-sparse Regularized Randomized Reduction. | Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu |
| 2015 | KDD | Big Data Analytics: Optimization and Randomization. | Tianbao Yang, Qihang Lin, Rong Jin |
| 2015 | KDD | An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints. | Jinfeng Yi, Lijun Zhang, Tianbao Yang, Wei Liu, Jun Wang |
| 2014 | AISTATS | Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs. | Jianhui Chen, Tianbao Yang, Shenghuo Zhu |
| 2013 | COLT | Recovering the Optimal Solution by Dual Random Projection. | Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu |
| 2013 | ICML | O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions. | Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He |
| 2012 | AAAI | Online Kernel Selection: Algorithms and Evaluations. | Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi |
| 2012 | ICDM | Robust Ensemble Clustering by Matrix Completion. | Jinfeng Yi, Tianbao Yang, Rong Jin, Anil K. Jain, Mehrdad Mahdavi |
| 2012 | ICML | A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound. | Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han |
| 2012 | ICML | Multiple Kernel Learning from Noisy Labels by Stochastic Programming. | Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou |
| 2011 | ICML | Online AUC Maximization. | Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang |
| 2010 | ALT | Online Multiple Kernel Learning: Algorithms and Mistake Bounds. | Rong Jin, Steven C. H. Hoi, Tianbao Yang |
| 2010 | ICML | Learning from Noisy Side Information by Generalized Maximum Entropy Model. | Tianbao Yang, Rong Jin, Anil K. Jain |
| 2010 | KDD | Unsupervised transfer classification: application to text categorization. | Tianbao Yang, Rong Jin, Anil K. Jain, Yang Zhou, Wei Tong |
| 2010 | SDM | Directed Network Community Detection: A Popularity and Productivity Link Model. | Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin |
| 2009 | KDD | Combining link and content for community detection: a discriminative approach. | Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu |
| 2009 | UAI | A Bayesian Framework for Community Detection Integrating Content and Link. | Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu |
| 2009 | SDM | A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks. | Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin |