World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
7716
World Ranking
6572
National Ranking
2907

Overview

Tianbao Yang is affiliated with Texas A&M University in the United States, with a research focus primarily in computer science. Their work spans multiple subfields including artificial intelligence, computational mechanics, computer vision and pattern recognition, management science and operations research, as well as statistics and probability.

The main topics explored in their research include stochastic gradient optimization techniques, sparse and compressive sensing techniques, machine learning and algorithms, machine learning and extreme learning machines, advanced bandit algorithms research, imbalanced data classification techniques, and machine learning and data classification.

Among recent publications, notable papers authored by or involving Tianbao Yang are:

  • "AUC Maximization in the Era of Big Data and AI: A Survey" (2022, ACM Computing Surveys)
  • "Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification" (2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))
  • "Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning" (2021, Optimization methods & software)
  • "Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence" (2021, arXiv (Cornell University))
  • "Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization" (2020, arXiv (Cornell University))

Frequent coauthors collaborating with Tianbao Yang include:

  • Zhishuai Guo
  • Zhuoning Yuan
  • Qihang Lin
  • Quanqi Hu
  • Yiming Ying

Yang's work has been published mainly in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Machine Learning
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • ACM Computing Surveys

Best Publications

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

    Tianbao Yang;Rong Jin;Yun Chi;Shenghuo Zhu

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

    Tianbao Yang;Yun Chi;Shenghuo Zhu;Yihong Gong

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

    Zhuoning Yuan;Xun Zhou;Tianbao Yang

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

    Tianbao Yang;Yu-feng Li;Mehrdad Mahdavi;Rong Jin

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

    Unknown

  • Online AUC Maximization

    Peilin Zhao;Rong Jin;Tianbao Yang;Steven C. Hoi

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

    Saining Xie;Tianbao Yang;Xiaoyu Wang;Yuanqing Lin

  • Learning Attributes Equals Multi-Source Domain Generalization

    Chuang Gan;Tianbao Yang;Boqing Gong

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

    Mehrdad Mahdavi;Rong Jin;Tianbao Yang

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

    Dixian Zhu;Changjie Cai;Tianbao Yang;Xun Zhou

  • Online Optimization with Gradual Variations

    Chao Kai Chiang;Chao Kai Chiang;Tianbao Yang;Chia Jung Lee;Mehrdad Mahdavi

  • Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent

    Tianbao Yang

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

    Hassan Rafique;Mingrui Liu;Qihang Lin;Tianbao Yang

  • Online Multiple Kernel Classification

    Steven C. Hoi;Rong Jin;Peilin Zhao;Tianbao Yang

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

    Hassan Rafique;Mingrui Liu;Qihang Lin;Tianbao Yang

  • Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

    Tianbao Yang;Qihang Lin;Zhe Li

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

    Yi Xu;Rong Jin;Tianbao Yang

  • A Unified Analysis of Stochastic Momentum Methods for Deep Learning

    Yan Yan;Yan Yan;Tianbao Yang;Zhe Li;Qihang Lin

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

    Jinfeng Yi;Rong Jin;Shaili Jain;Tianbao Yang

  • Online multiple kernel learning: algorithms and mistake bounds

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

  • Tracking slowly moving clairvoyant: optimal dynamic regret of online learning with true and noisy gradient

    Tianbao Yang;Lijun Zhang;Rong Jin;Jinfeng Yi

  • Improved Dynamic Regret for Non-degenerate Functions

    Lijun Zhang;Tianbao Yang;Jinfeng Yi;Rong Jin

  • Dynamic Regret of Strongly Adaptive Methods

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

Frequent Co-Authors

Rong Jin
Rong Jin Alibaba Group (China)
Yan Yan
Yan Yan Illinois Institute of Technology
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Jinfeng Yi
Jinfeng Yi IBM (United States)
Boqing Gong
Boqing Gong Google (United States)
Wei Liu
Wei Liu Tencent (China)
Anil K. Jain
Anil K. Jain Michigan State University
Yuanqing Lin
Yuanqing Lin Aibee Inc.
Tajana Rosing
Tajana Rosing University of California, San Diego

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