World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
28088
World Ranking
1740
National Ranking
885

Overview

Xiaojin Zhu is affiliated with the University of Wisconsin-Madison in the United States. Their work intersects the fields of Computer Science and Engineering, with a focus spanning several specialized subfields including Artificial Intelligence, Control and Systems Engineering, Clinical Psychology, Management Science and Operations Research, and Computer Vision and Pattern Recognition.

The scientist's research topics cover areas such as Reinforcement Learning in Robotics, Adversarial Robustness in Machine Learning, Machine Learning and Algorithms, Machine Learning and Data Classification, Advanced Bandit Algorithms Research, Child and Adolescent Psychosocial and Emotional Development, and Adaptive Control of Nonlinear Systems.

Xiaojin Zhu has contributed to numerous publications, some of the recent ones include:

  • Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest, 2020, Environmental Research Letters
  • Differential Patterns of Delayed Emotion Circuit Maturation in Abused Girls With and Without Internalizing Psychopathology, 2021, American Journal of Psychiatry
  • Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning, 2020, arXiv (Cornell University)
  • Adaptive Reward-Poisoning Attacks against Reinforcement Learning, 2020, arXiv (Cornell University)
  • Robust Policy Gradient against Strong Data Corruption, 2021, arXiv (Cornell University)

The frequent publication venues for the scientist include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Environmental Research Letters
  • American Journal of Psychiatry
  • 2022 European Control Conference (ECC)

Collaborations have been established with several researchers, including:

  • Adish Singla
  • Yuzhe Ma
  • Xuezhou Zhang
  • Shubham Bharti
  • Taylor J. Keding

Best Publications

  • Semi-Supervised Learning Literature Survey

    Xiaojin Zhu

  • Semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;Zoubin Ghahramani;John Lafferty

  • Introduction to Semi-Supervised Learning

    Xiaojin Zhu;Andrew B. Goldberg;Ronald Brachman;Thomas Dietterich

  • Learning from labeled and unlabeled data with label propagation

    X Zhu;Z Ghahramani

  • Semi-supervised learning with graphs

    Xiaojin Zhu;John Lafferty;Ronald Rosenfeld

  • Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;John Lafferty;Zoubin Ghahramani

  • Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

    David Andrzejewski;Xiaojin Zhu;Mark Craven

  • Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization

    Andrew Goldberg;Xiaojin Zhu

  • Learning from Bullying Traces in Social Media

    Jun-Ming Xu;Kwang-Sung Jun;Xiaojin Zhu;Amy Bellmore

  • Using machine teaching to identify optimal training-set attacks on machine learners

    Shike Mei;Xiaojin Zhu

  • A Topic Model for Word Sense Disambiguation

    Jordan Boyd-Graber;David Blei;Xiaojin Zhu

  • Improving Diversity in Ranking using Absorbing Random Walks

    Xiaojin Zhu;Andrew Goldberg;Jurgen Van Gael;David Andrzejewski

  • Corleone: hands-off crowdsourcing for entity matching

    Chaitanya Gokhale;Sanjib Das;AnHai Doan;Jeffrey F. Naughton

  • Machine teaching: an inverse problem to machine learning and an approach toward optimal education

    Xiaojin Zhu

  • Unlabeled data: Now it helps, now it doesn't

    Aarti Singh;Robert Nowak;Xiaojin Zhu

  • Segmenting hands of arbitrary color

    Xiaojin Zhu;Jie Yang;A. Waibel

  • Transduction with Matrix Completion: Three Birds with One Stone

    Andrew Goldberg;Ben Recht;Junming Xu;Robert Nowak

  • Data poisoning attacks against autoregressive models

    Scott Alfeld;Xiaojin Zhu;Paul Barford

  • Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning

    Xiaojin Zhu;John Lafferty

  • Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

    Yanghui Kang;Mutlu Ozdogan;Xiaojin Zhu;Zhiwei Ye

  • Kernel conditional random fields: representation and clique selection

    John Lafferty;Xiaojin Zhu;Yan Liu

Frequent Co-Authors

Timothy T. Rogers
Timothy T. Rogers University of Wisconsin–Madison
John Lafferty
John Lafferty Yale University
Robert Nowak
Robert Nowak University of Wisconsin–Madison
Charles W. Kalish
Charles W. Kalish University of Wisconsin–Madison
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Paul Barford
Paul Barford University of Wisconsin–Madison
Amy Bellmore
Amy Bellmore University of Wisconsin–Madison
Charles R. Dyer
Charles R. Dyer University of Wisconsin–Madison
Roni Rosenfeld
Roni Rosenfeld Carnegie Mellon University
Alex Waibel
Alex Waibel Carnegie Mellon University

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