ML Anthology

Yang, Tianbao

155 publications

TMLR 2026 Single-Loop Algorithms for Stochastic Non-Convex Optimization with Weakly-Convex Constraints Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang
ICML 2025 A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization Bokun Wang, Tianbao Yang
NeurIPS 2025 Advancing Interpretability of CLIP Representations with Concept Surrogate Model Nhat Hoang-Xuan, Xiyuan Wei, Wanli Xing, Tianbao Yang, My T. Thai
TMLR 2025 AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving 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
NeurIPS 2025 DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization Gang Li, Ming Lin, Tomer Galanti, Zhengzhong Tu, Tianbao Yang
ICML 2025 Discovering Global False Negatives on the Fly for Self-Supervised Contrastive Learning Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang
ICML 2025 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
ICML 2025 Gradient Aligned Regression via Pairwise Losses Dixian Zhu, Tianbao Yang, Livnat Jerby
TMLR 2025 Learning to Rank with Top-$k$ Fairness Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang
ICML 2025 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
TMLR 2025 Multi-Output Distributional Fairness via Post-Processing Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang
ICLR 2025 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang
MLJ 2025 Optimal Large-Scale Stochastic Optimization of NDCG Surrogates for Deep Learning Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Wei-Wei Tu, Lijun Zhang, Tianbao Yang
NeurIPS 2025 Self-Supervised Contrastive Learning Is Approximately Supervised Contrastive Learning Achleshwar Luthra, Tianbao Yang, Tomer Galanti
NeurIPS 2025 Stochastic Momentum Methods for Non-Smooth Non-Convex Finite-Sum Coupled Compositional Optimization Xingyu Chen, Bokun Wang, Ming Yang, Qihang Lin, Tianbao Yang
TMLR 2025 Stochastic Primal-Dual Double Block-Coordinate for Two- Way Partial AUC Maximization Linli Zhou, Bokun Wang, My T. Thai, Tianbao Yang
JMLR 2025 Universal Online Convex Optimization Meets Second-Order Bounds Lijun Zhang, Yibo Wang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
NeurIPS 2024 Adaptive Preference Scaling for Reinforcement Learning with Human Feedback Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao
NeurIPS 2024 Communication-Efficient Federated Group Distributionally Robust Optimization Zhishuai Guo, Tianbao Yang
NeurIPSW 2024 Memory Efficient Continual Learning with CLIP Models Ryan King, Gang Li, Bobak J Mortazavi, Tianbao Yang
NeurIPSW 2024 Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang
NeurIPSW 2024 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang
NeurIPS 2024 Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang
ICML 2024 Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
ICML 2024 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
NeurIPSW 2024 Which LLMs Are Difficult to Detect? a Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection Shantanu Thorat, Tianbao Yang
TMLR 2023 Attentional-Biased Stochastic Gradient Descent Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
ICML 2023 Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
JMLR 2023 Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
ICML 2023 FeDXL: Provable Federated Learning for Deep X-Risk Optimization Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
NeurIPS 2023 Federated Compositional Deep AUC Maximization Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
ICML 2023 Generalization Analysis for Contrastive Representation Learning Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
ICML 2023 Label Distributionally Robust Losses for Multi-Class Classification: Consistency, Robustness and Adaptivity Dixian Zhu, Yiming Ying, Tianbao Yang
ICML 2023 Learning Unnormalized Statistical Models via Compositional Optimization Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
NeurIPS 2023 Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness Gang Li, Wei Tong, Tianbao Yang
JMLR 2023 Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
NeurIPS 2023 Non-Smooth Weakly-Convex Finite-Sum Coupled Compositional Optimization Quanqi Hu, Dixian Zhu, 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, Tianbao Yang
ICML 2023 Provable Multi-Instance Deep AUC Maximization with Stochastic Pooling Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
NeurIPS 2023 SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang
NeurIPS 2023 Stochastic Approximation Approaches to Group Distributionally Robust Optimization Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou
TMLR 2023 Stochastic Constrained DRO with a Complexity Independent of Sample Size Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang
AISTATS 2023 Stochastic Methods for AUC Optimization Subject to AUC-Based Fairness Constraints Yao Yao, Qihang Lin, Tianbao Yang
AISTATS 2022 Momentum Accelerates the Convergence of Stochastic AUPRC Maximization Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang
ICML 2022 A Simple yet Universal Strategy for Online Convex Optimization Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang
ICLR 2022 Compositional Training for End-to-End Deep AUC Maximization Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang
ICML 2022 Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications Bokun Wang, Tianbao Yang
ICML 2022 GraphFM: Improving Large-Scale GNN Training via Feature Momentum Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
NeurIPS 2022 Large-Scale Optimization of Partial AUC in a Range of False Positive Rates Yao Yao, Qihang Lin, Tianbao Yang
ICML 2022 Large-Scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang
NeurIPS 2022 Multi-Block Min-Max Bilevel Optimization with Applications in Multi-Task Deep AUC Maximization Quanqi Hu, Yongjian Zhong, Tianbao Yang
NeurIPS 2022 Multi-Block-Single-Probe Variance Reduced Estimator for Coupled Compositional Optimization Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang
ICML 2022 Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
ICML 2022 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
NeurIPS 2022 Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang
ICML 2022 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
NeurIPSW 2021 A Unified DRO View of Multi-Class Loss Functions with Top-N Consistency Dixian Zhu, Tianbao Yang
NeurIPS 2021 An Online Method for a Class of Distributionally Robust Optimization with Non-Convex Objectives Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
ICML 2021 Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
JMLR 2021 First-Order Convergence Theory for Weakly-Convex-Weakly-Concave Min-Max Problems Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
ICCV 2021 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
NeurIPS 2021 Online Convex Optimization with Continuous Switching Constraint Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
NeurIPS 2021 Revisiting Smoothed Online Learning Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
NeurIPS 2021 Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
ICML 2021 Stability and Generalization of Stochastic Gradient Methods for Minimax Problems Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying
NeurIPS 2021 Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
JMLR 2020 A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
NeurIPS 2020 A Decentralized Parallel Algorithm for Training Generative Adversarial Nets Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
ECCV 2020 A Simple and Effective Framework for Pairwise Deep Metric Learning Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang
ECCV 2020 Accelerating Deep Learning with Millions of Classes Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
AAAI 2020 Adversarial Localized Energy Network for Structured Prediction Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang
ICLR 2020 Attacking Lifelong Learning Models with Gradient Reversion Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing
ICML 2020 Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
AAAI 2020 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
MLJ 2020 High-Dimensional Model Recovery from Random Sketched Data by Exploring Intrinsic Sparsity Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin
NeurIPS 2020 Improved Schemes for Episodic Memory-Based Lifelong Learning Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
AISTATS 2020 Minimizing Dynamic Regret and Adaptive Regret Simultaneously Lijun Zhang, Shiyin Lu, Tianbao Yang
NeurIPS 2020 Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
ICML 2020 Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints Runchao Ma, Qihang Lin, Tianbao Yang
ICLR 2020 Stochastic AUC Maximization with Deep Neural Networks Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang
ICML 2020 Stochastic Optimization for Non-Convex Inf-Projection Problems Yan Yan, Yi Xu, Lijun Zhang, Wang Xiaoyu, Tianbao Yang
ICLR 2020 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
AISTATS 2019 A Robust Zero-Sum Game Framework for Pool-Based Active Learning Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang
MLJ 2019 A Simple Homotopy Proximal Mapping Algorithm for Compressive Sensing Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
ICML 2019 Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang
UAI 2019 Learning with Non-Convex Truncated Losses by SGD Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
NeurIPS 2019 Non-Asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems Yi Xu, Rong Jin, Tianbao Yang
IJCAI 2019 On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang
JMLR 2019 Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
NeurIPS 2019 Stagewise Training Accelerates Convergence of Testing Error over SGD Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang
ICML 2019 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
ICLR 2019 Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang
IJCAI 2018 A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang
AISTATS 2018 A Simple Analysis for Exp-Concave Empirical Minimization with Arbitrary Convex Regularizer Tianbao Yang, Zhe Li, Lijun Zhang
IJCAI 2018 A Unified Analysis of Stochastic Momentum Methods for Deep Learning Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
NeurIPS 2018 Adaptive Negative Curvature Descent with Applications in Non-Convex Optimization Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
ICML 2018 Dynamic Regret of Strongly Adaptive Methods Lijun Zhang, Tianbao Yang, Jin, Zhi-Hua Zhou
NeurIPS 2018 Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
ICML 2018 Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang
NeurIPS 2018 Faster Online Learning of Optimal Threshold for Consistent F-Measure Optimization Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
NeurIPS 2018 First-Order Stochastic Algorithms for Escaping from Saddle Points in Almost Linear Time Yi Xu, Rong Jin, Tianbao Yang
ECCV 2018 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
ECCV 2018 Improving Sequential Determinantal Point Processes for Supervised Video Summarization Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong
ICML 2018 Level-Set Methods for Finite-Sum Constrained Convex Optimization Qihang Lin, Runchao Ma, Tianbao Yang
JMLR 2018 RSG: Beating Subgradient Method Without Smoothness and Strong Convexity Tianbao Yang, Qihang Lin
ICML 2018 SADAGRAD: Strongly Adaptive Stochastic Gradient Methods Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang
AAAI 2017 A Framework of Online Learning with Imbalanced Streaming Data Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen
ICML 2017 A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates Tianbao Yang, Qihang Lin, Lijun Zhang
AAAI 2017 A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin
NeurIPS 2017 ADMM Without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
NeurIPS 2017 Adaptive Accelerated Gradient Converging Method Under H\"olderian Error Bound Condition Mingrui Liu, Tianbao Yang
NeurIPS 2017 Adaptive SVRG Methods Under Error Bound Conditions with Unknown Growth Parameter Yi Xu, Qihang Lin, Tianbao Yang
JMLR 2017 Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
AAAI 2017 Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang
COLT 2017 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
NeurIPS 2017 Improved Dynamic Regret for Non-Degenerate Functions Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
IJCAI 2017 SVD-Free Convex-Concave Approaches for Nuclear Norm Regularization Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang
ICML 2017 Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence Yi Xu, Qihang Lin, Tianbao Yang
AAAI 2016 Fast and Accurate Refined Nyström-Based Kernel SVM Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin
NeurIPS 2016 Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$ Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
NeurIPS 2016 Improved Dropout for Shallow and Deep Learning Zhe Li, Boqing Gong, Tianbao Yang
CVPR 2016 Learning Attributes Equals Multi-Source Domain Generalization Chuang Gan, Tianbao Yang, Boqing Gong
MLJ 2016 On Data Preconditioning for Regularized Loss Minimization Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
ICML 2016 Online Stochastic Linear Optimization Under One-Bit Feedback Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou
UAI 2016 Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang
ALT 2016 Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
AAAI 2016 Stochastic Optimization for Kernel PCA Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
ICML 2016 Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi
AISTATS 2015 A Simple Homotopy Algorithm for Compressive Sensing Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
MLJ 2015 An Efficient Primal Dual Prox Method for Non-Smooth Optimization Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
ICML 2015 An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
CVPR 2015 Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin
AAAI 2015 Online Bandit Learning for a Special Class of Non-Convex Losses Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
ICML 2015 Theory of Dual-Sparse Regularized Randomized Reduction Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
AISTATS 2014 Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs Jianhui Chen, Tianbao Yang, Shenghuo Zhu
NeurIPS 2014 Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
MLJ 2014 Regret Bounded by Gradual Variation for Online Convex Optimization Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
ICML 2013 O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He
MLJ 2013 Online Multiple Kernel Classification Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang
COLT 2013 Recovering the Optimal Solution by Dual Random Projection Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu
NeurIPS 2013 Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
NeurIPS 2013 Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang
ICML 2012 A Simple Algorithm for Semi-Supervised Learning with Improved Generalization Error Bound Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han
ICML 2012 Multiple Kernel Learning from Noisy Labels by Stochastic Programming Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou
NeurIPS 2012 Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison Tianbao Yang, Yu-feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou
AAAI 2012 Online Kernel Selection: Algorithms and Evaluations Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
COLT 2012 Online Optimization with Gradual Variations Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu
NeurIPS 2012 Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain
NeurIPS 2012 Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi
JMLR 2012 Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints Mehrdad Mahdavi, Rong Jin, Tianbao Yang
MLJ 2011 Detecting Communities and Their Evolutions in Dynamic Social Networks - A Bayesian Approach Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin
ICML 2011 Online AUC Maximization Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang
ICML 2010 Learning from Noisy Side Information by Generalized Maximum Entropy Model Tianbao Yang, Rong Jin, Anil K. Jain
ALT 2010 Online Multiple Kernel Learning: Algorithms and Mistake Bounds Rong Jin, Steven C. H. Hoi, Tianbao Yang
UAI 2009 A Bayesian Framework for Community Detection Integrating Content and Link Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu