Tianbao Yang

According to our database, Tianbao Yang authored at least 217 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning.

Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang

J. Mach. Learn. Res., 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

J. Mach. Learn. Res., 2023

AUC Maximization in the Era of Big Data and AI: A Survey.

Tianbao Yang, Yiming Ying

ACM Comput. Surv., 2023

ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization.

Bokun Wang, Tianbao Yang

CoRR, 2023

AUC-mixup: Deep AUC Maximization with Mixup.

Jianzhi Xv, Gang Li, Tianbao Yang

CoRR, 2023

Everything Perturbed All at Once: Enabling Differentiable Graph Attacks.

Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee

CoRR, 2023

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms.

Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying

CoRR, 2023

Learning Unnormalized Statistical Models via Compositional Optimization.

Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang

CoRR, 2023

Stochastic Approximation Approaches to Group Distributionally Robust Optimization.

Lijun Zhang, Peng Zhao, Tianbao Yang, Zhi-Hua Zhou

CoRR, 2023

Federated Compositional Deep AUC Maximization.

Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness.

Gang Li, Wei Tong, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization.

Quanqi Hu, Dixian Zhu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data.

Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Approximation Approaches to Group Distributionally Robust Optimization.

Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multimodal Pretraining of Medical Time Series and Notes.

Ryan King, Tianbao Yang, Bobak J. Mortazavi

Proceedings of the Machine Learning for Health, 2023

LibAUC: A Deep Learning Library for X-Risk Optimization.

Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity.

Dixian Zhu, Yiming Ying, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 2023

Provable Multi-instance Deep AUC Maximization with Stochastic Pooling.

Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Generalization Analysis for Contrastive Representation Learning.

Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou

Proceedings of the International Conference on Machine Learning, 2023

Learning Unnormalized Statistical Models via Compositional Optimization.

Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

FeDXL: Provable Federated Learning for Deep X-Risk Optimization.

Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 2023

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints.

Yao Yao, Qihang Lin, Tianbao Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification.

Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, Hao Sun, Tianbao Yang, Changwei Tan

Remote. Sens., 2022

Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning.

Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang

Optim. Methods Softw., 2022

FedX: Federated Learning for Compositional Pairwise Risk Optimization.

Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang

CoRR, 2022

Fairness via Adversarial Attribute Neighbourhood Robust Learning.

Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang

CoRR, 2022

Stochastic Constrained DRO with a Complexity Independent of Sample Size.

Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang

CoRR, 2022

Algorithmic Foundation of Deep X-Risk Optimization.

Tianbao Yang

CoRR, 2022

Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices.

Dixian Zhu, Xiaodong Wu, Tianbao Yang

CoRR, 2022

Finite-Sum Compositional Stochastic Optimization: Theory and Applications.

Bokun Wang, Tianbao Yang

CoRR, 2022

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor.

Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Large-scale Optimization of Partial AUC in a Range of False Positive Rates.

Yao Yao, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization.

Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization.

Quanqi Hu, Yongjian Zhong, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

A Simple yet Universal Strategy for Online Convex Optimization.

Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2022

GraphFM: Improving Large-Scale GNN Training via Feature Momentum.

Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2022

Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications.

Bokun Wang, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2022

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization.

Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 2022

Compositional Training for End-to-End Deep AUC Maximization.

Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Momentum Accelerates the Convergence of Stochastic AUPRC Maximization.

Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems.

Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

J. Mach. Learn. Res., 2021

Hybrid safe-strong rules for efficient optimization in lasso-type problems.

Yaohui Zeng, Tianbao Yang, Patrick Breheny

Comput. Stat. Data Anal., 2021

A Unified DRO View of Multi-class Loss Functions with top-N Consistency.

Dixian Zhu, Tianbao Yang

CoRR, 2021

A Novel Convergence Analysis for Algorithms of the Adam Family.

Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

CoRR, 2021

Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities.

Tianbao Yang

CoRR, 2021

Memory-based Optimization Methods for Model-Agnostic Meta-Learning.

Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang

CoRR, 2021

Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization.

Zhishuai Guo, Tianbao Yang

CoRR, 2021

On Stochastic Moving-Average Estimators for Non-Convex Optimization.

Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

CoRR, 2021

Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence.

Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang

CoRR, 2021

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

CoRR, 2021

Revisiting Smoothed Online Learning.

Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning.

Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online Convex Optimization with Continuous Switching Constraint.

Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence.

Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

Proceedings of the 38th International Conference on Machine Learning, 2021

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems.

Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.

Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin

Mach. Learn., 2020

Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent.

Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi

J. Parallel Distributed Comput., 2020

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.

Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang

J. Mach. Learn. Res., 2020

Attentional Biased Stochastic Gradient for Imbalanced Classification.

Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang

CoRR, 2020

Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification.

Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang

CoRR, 2020

Adam⁺: A Stochastic Method with Adaptive Variance Reduction.

Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang

CoRR, 2020

Variance-Reduced Off-Policy Memory-Efficient Policy Search.

Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu

CoRR, 2020

A Practical Online Method for Distributionally Deep Robust Optimization.

Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang

CoRR, 2020

Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise.

Yan Yan, Xin Man, Tianbao Yang

CoRR, 2020

Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems.

Zhishuai Guo, Zhuoning Yuan, Yan Yan, Tianbao Yang

CoRR, 2020

Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives.

Zhishuai Guo, Zixuan Wu, Yan Yan, Xiaoyu Wang, Tianbao Yang

CoRR, 2020

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

CoRR, 2020

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets.

Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Schemes for Episodic Memory-based Lifelong Learning.

Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints.

Runchao Ma, Qihang Lin, Tianbao Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks.

Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Optimization for Non-convex Inf-Projection Problems.

Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic AUC Maximization with Deep Neural Networks.

Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang

Proceedings of the 8th International Conference on Learning Representations, 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

Proceedings of the 8th International Conference on Learning Representations, 2020

Accelerating Deep Learning with Millions of Classes.

Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang

Proceedings of the Computer Vision - ECCV 2020, 2020

A Simple and Effective Framework for Pairwise Deep Metric Learning.

Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang

Proceedings of the Computer Vision - ECCV 2020, 2020

Minimizing Dynamic Regret and Adaptive Regret Simultaneously.

Lijun Zhang, Shiyin Lu, Tianbao Yang

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 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

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Adversarial Localized Energy Network for Structured Prediction.

Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

A simple homotopy proximal mapping algorithm for compressive sensing.

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou

Mach. Learn., 2019

Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

J. Mach. Learn. Res., 2019

Decentralized Parallel Algorithm for Training Generative Adversarial Nets.

Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das

CoRR, 2019

Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient.

Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

CoRR, 2019

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure.

Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang

CoRR, 2019

Advancing non-convex and constrained learning: challenges and opportunities.

Tianbao Yang

AI Matters, 2019

Learning with Non-Convex Truncated Losses by SGD.

Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Stagewise Training Accelerates Convergence of Testing Error Over SGD.

Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems.

Yi Xu, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization.

Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 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

Proceedings of the 36th International Conference on Machine Learning, 2019

Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number.

Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang

Proceedings of the 36th International Conference on Machine Learning, 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

Proceedings of the 7th International Conference on Learning Representations, 2019

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

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

A Robust Zero-Sum Game Framework for Pool-based Active Learning.

Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Combining Link and Content for Community Detection.

Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu

Proceedings of the Encyclopedia of Social Network Analysis and Mining, 2nd Edition, 2018

RSG: Beating Subgradient Method without Smoothness and Strong Convexity.

Tianbao Yang, Qihang Lin

J. Mach. Learn. Res., 2018

Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD?

Tianbao Yang, Yan Yan, Zhuoning Yuan, Rong Jin

CoRR, 2018

Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.

Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang

CoRR, 2018

Learning Discriminators as Energy Networks in Adversarial Learning.

Pingbo Pan, Yan Yan, Tianbao Yang, Yi Yang

CoRR, 2018

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching.

Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran

CoRR, 2018

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints.

Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang

CoRR, 2018

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization.

Dixian Zhu, Changjie Cai, Tianbao Yang, Xun Zhou

Big Data Cogn. Comput., 2018

Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization.

Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time.

Yi Xu, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions.

Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Adaptive Negative Curvature Descent with Applications in Non-convex Optimization.

Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data.

Zhuoning Yuan, Xun Zhou, Tianbao Yang

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization.

Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

A Unified Analysis of Stochastic Momentum Methods for Deep Learning.

Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Dynamic Regret of Strongly Adaptive Methods.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

Proceedings of the 35th International Conference on Machine Learning, 2018

Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate.

Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang

Proceedings of the 35th International Conference on Machine Learning, 2018

Level-Set Methods for Finite-Sum Constrained Convex Optimization.

Qihang Lin, Runchao Ma, Tianbao Yang

Proceedings of the 35th International Conference on Machine Learning, 2018

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods.

Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang

Proceedings of the 35th International Conference on Machine Learning, 2018

Improving Sequential Determinantal Point Processes for Supervised Video Summarization.

Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong

Proceedings of the Computer Vision - ECCV 2018, 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

Proceedings of the Computer Vision - ECCV 2018, 2018

An Indirect-Direct event-triggered mechanism for networked control system against DoS attacks.

Zihao Cheng, Dong Yue, Songlin Hu, Tianbao Yang, Lei Chen

Proceedings of the 2018 Australian & New Zealand Control Conference (ANZCC), 2018

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer.

Tianbao Yang, Zhe Li, Lijun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

A Multisource Domain Generalization Approach to Visual Attribute Detection.

Chuang Gan, Tianbao Yang, Boqing Gong

Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.

Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

J. Mach. Learn. Res., 2017

Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence.

Mingrui Liu, Tianbao Yang

CoRR, 2017

SEP-Nets: Small and Effective Pattern Networks.

Zhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang

CoRR, 2017

Strongly Adaptive Regret Implies Optimally Dynamic Regret.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

CoRR, 2017

Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n²)-type of Risk Bounds.

Lijun Zhang, Tianbao Yang, Rong Jin

CoRR, 2017

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization.

Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition.

Mingrui Liu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Improved Dynamic Regret for Non-degenerate Functions.

Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

SVD-free Convex-Concave Approaches for Nuclear Norm Regularization.

Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates.

Tianbao Yang, Qihang Lin, Lijun Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the 34th International Conference on Machine Learning, 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

Proceedings of the 30th Conference on Learning Theory, 2017

A Framework of Online Learning with Imbalanced Streaming Data.

Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee.

Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis.

Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

On Data Preconditioning for Regularized Loss Minimization.

Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin

Mach. Learn., 2016

Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound.

Yi Xu, Qihang Lin, Tianbao Yang

CoRR, 2016

Improved dynamic regret for non-degeneracy functions.

Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou

CoRR, 2016

Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections.

Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon).

Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improved Dropout for Shallow and Deep Learning.

Zhe Li, Boqing Gong, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Online Asymmetric Active Learning with Imbalanced Data.

Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Online Stochastic Linear Optimization under One-bit Feedback.

Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou

Proceedings of the 33nd International Conference on Machine Learning, 2016

Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient.

Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi

Proceedings of the 33nd International Conference on Machine Learning, 2016

Learning Attributes Equals Multi-Source Domain Generalization.

Chuang Gan, Tianbao Yang, Boqing Gong

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 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

Proceedings of the Algorithmic Learning Theory - 27th International Conference, 2016

Fast and Accurate Refined Nyström-Based Kernel SVM.

Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Stochastic Optimization for Kernel PCA.

Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

An efficient primal dual prox method for non-smooth optimization.

Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu

Mach. Learn., 2015

Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data.

Adams Wei Yu, Qihang Lin, Tianbao Yang

CoRR, 2015

Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees.

Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin

CoRR, 2015

Stochastic Proximal Gradient Descent for Nuclear Norm Regularization.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

CoRR, 2015

Online Stochastic Linear Optimization under One-bit Feedback.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

CoRR, 2015

Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

CoRR, 2015

An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints.

Jinfeng Yi, Lijun Zhang, Tianbao Yang, Wei Liu, Jun Wang

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Big Data Analytics: Optimization and Randomization.

Tianbao Yang, Qihang Lin, Rong Jin

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Theory of Dual-sparse Regularized Randomized Reduction.

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

Proceedings of the 32nd International Conference on Machine Learning, 2015

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection.

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

Proceedings of the 32nd International Conference on Machine Learning, 2015

In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones.

Syed Shabih Hasan, Ryan Brummet, Octav Chipara, Yu-Hsiang Wu, Tianbao Yang

Proceedings of the 2015 International Conference on Healthcare Informatics, 2015

Hyper-class augmented and regularized deep learning for fine-grained image classification.

Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

A Simple Homotopy Algorithm for Compressive Sensing.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Online Bandit Learning for a Special Class of Non-Convex Losses.

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Combining Link and Content for Community Detection.

Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu

Encyclopedia of Social Network Analysis and Mining, 2014

Random Projections for Classification: A Recovery Approach.

Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu

IEEE Trans. Inf. Theory, 2014

Regret bounded by gradual variation for online convex optimization.

Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu

Mach. Learn., 2014

On Data Preconditioning for Regularized Loss Minimization.

Tianbao Yang, Rong Jin, Shenghuo Zhu

CoRR, 2014

A Simple Homotopy Proximal Mapping for Compressive Sensing.

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

CoRR, 2014

Object-centric Sampling for Fine-grained Image Classification.

Xiaoyu Wang, Tianbao Yang, Guobin Chen, Yuanqing Lin

CoRR, 2014

Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities.

Tianbao Yang, Rong Jin

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs.

Jianhui Chen, Tianbao Yang, Shenghuo Zhu

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Improved Bounds for the Nyström Method With Application to Kernel Classification.

Rong Jin, Tianbao Yang, Mehrdad Mahdavi, Yu-Feng Li, Zhi-Hua Zhou

IEEE Trans. Inf. Theory, 2013

Online Multiple Kernel Classification.

Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang

Mach. Learn., 2013

Efficient Stochastic Gradient Descent for Strongly Convex Optimization

Tianbao Yang, Lijun Zhang

CoRR, 2013

A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent

Rong Jin, Tianbao Yang, Shenghuo Zhu

CoRR, 2013

Sparse Multiple Kernel Learning with Geometric Convergence Rate

Rong Jin, Tianbao Yang, Mehrdad Mahdavi

CoRR, 2013

On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent.

Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin

CoRR, 2013

Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent.

Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Stochastic Convex Optimization with Multiple Objectives.

Mehrdad Mahdavi, Tianbao Yang, Rong Jin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Directed Inference Approach towards Multi-class Multi-model Fusion.

Tianbao Yang, Lei Wu, Piero P. Bonissone

Proceedings of the Multiple Classifier Systems, 11th International Workshop, 2013

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions.

Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He

Proceedings of the 30th International Conference on Machine Learning, 2013

Recovering the Optimal Solution by Dual Random Projection.

Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu

Proceedings of the COLT 2013, 2013

Community detection by popularity based models for authored networked data.

Tianbao Yang, Prakash Mandayam Comar, Linli Xu

Proceedings of the Advances in Social Networks Analysis and Mining 2013, 2013

2012

Trading regret for efficiency: online convex optimization with long term constraints.

Mehrdad Mahdavi, Rong Jin, Tianbao Yang

J. Mach. Learn. Res., 2012

Online Optimization with Gradual Variations.

Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu

Proceedings of the COLT 2012, 2012

Influence Analysis in the Blogosphere

Michinari Momma, Yun Chi, Yuanqing Lin, Shenghuo Zhu, Tianbao Yang

CoRR, 2012

Online Stochastic Optimization with Multiple Objectives

Mehrdad Mahdavi, Tianbao Yang, Rong Jin

CoRR, 2012

Recovering Optimal Solution by Dual Random Projection

Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang

CoRR, 2012

An Improved Bound for the Nystrom Method for Large Eigengap

Mehrdad Mahdavi, Tianbao Yang, Rong Jin

CoRR, 2012

Efficient Constrained Regret Minimization

Mehrdad Mahdavi, Tianbao Yang, Rong Jin

CoRR, 2012

Learning kernel combination from noisy pairwise constraints.

Tianbao Yang, Rong Jin, Anil K. Jain

Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning.

Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison.

Tianbao Yang, Yu-Feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Stochastic Gradient Descent with Only One Projection.

Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Multiple Kernel Learning from Noisy Labels by Stochastic Programming.

Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou

Proceedings of the 29th International Conference on Machine Learning, 2012

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound.

Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han

Proceedings of the 29th International Conference on Machine Learning, 2012

Robust Ensemble Clustering by Matrix Completion.

Jinfeng Yi, Tianbao Yang, Rong Jin, Anil K. Jain, Mehrdad Mahdavi

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Online Kernel Selection: Algorithms and Evaluations.

Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

Detecting communities and their evolutions in dynamic social networks - a Bayesian approach.

Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin

Mach. Learn., 2011

Regret Bound by Variation for Online Convex Optimization

Tianbao Yang, Rong Jin, Mehrdad Mahdavi

CoRR, 2011

Improved Bound for the Nystrom's Method and its Application to Kernel Classification

Rong Jin, Tianbao Yang, Mehrdad Mahdavi

CoRR, 2011

A kernel density based approach for large scale image retrieval.

Wei Tong, Fengjie Li, Tianbao Yang, Rong Jin, Anil K. Jain

Proceedings of the 1st International Conference on Multimedia Retrieval, 2011

Online AUC Maximization.

Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Directed Network Community Detection: A Popularity and Productivity Link Model.

Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin

Proceedings of the SIAM International Conference on Data Mining, 2010

Unsupervised transfer classification: application to text categorization.

Tianbao Yang, Rong Jin, Anil K. Jain, Yang Zhou, Wei Tong

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Learning from Noisy Side Information by Generalized Maximum Entropy Model.

Tianbao Yang, Rong Jin, Anil K. Jain

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Online Multiple Kernel Learning: Algorithms and Mistake Bounds.

Rong Jin, Steven C. H. Hoi, Tianbao Yang

Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009

A Bayesian Framework for Community Detection Integrating Content and Link.

Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu

Proceedings of the UAI 2009, 2009

A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.

Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin

Proceedings of the SIAM International Conference on Data Mining, 2009

Combining link and content for community detection: a discriminative approach.

Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009