World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
17488
World Ranking
7373
National Ranking
3213

Overview

Dengyong Zhou is a researcher primarily affiliated with Google in the United States. Their academic work centers on computer science, with a focus on subfields including computer vision and pattern recognition, artificial intelligence, computer science applications, management science and operations research, and information systems.

Their research covers several key topics such as advanced image and video retrieval techniques, advanced neural network applications, domain adaptation and few-shot learning, mobile crowdsensing and crowdsourcing, auction theory and applications, privacy-preserving technologies in data, and information retrieval and search behavior.

Zhou has contributed to several recent publications. Notable papers include:

  • "Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision" (2021), published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Approval Voting and Incentives in Crowdsourcing" (2020), published in ACM Transactions on Economics and Computation
  • "Approval Voting and Incentives in Crowdsourcing" (2020), published in ACM Transactions on Economics and Computation
  • "Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision" (2020), published in arXiv (Cornell University)

Frequent coauthors in Zhou's work include:

  • Xingchao Liu
  • Nihar B. Shah
  • Mao Ye
  • Qiang Liu
  • Yuval Peres

Their research has been published predominantly in venues such as:

  • ACM Transactions on Economics and Computation
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • arXiv (Cornell University)

Best Publications

  • Learning with Local and Global Consistency

    Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston

  • Recommender systems with social regularization

    Hao Ma;Dengyong Zhou;Chao Liu;Michael R. Lyu

  • Learning with Hypergraphs: Clustering, Classification, and Embedding

    Dengyong Zhou;Jiayuan Huang;Bernhard Schölkopf

  • Ranking on Data Manifolds

    Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet

  • Semi-Supervised Graph-Based Hyperspectral Image Classification

    G. Camps-Valls;T. Bandos Marsheva;D. Zhou

  • Spectral clustering and transductive learning with multiple views

    Dengyong Zhou;Christopher J. C. Burges

  • Learning from labeled and unlabeled data on a directed graph

    Dengyong Zhou;Jiayuan Huang;Bernhard Schölkopf

  • Evolutionary spectral clustering by incorporating temporal smoothness

    Yun Chi;Xiaodan Song;Dengyong Zhou;Koji Hino

  • Query suggestion using hitting time

    Qiaozhu Mei;Dengyong Zhou;Kenneth Church

  • Learning from the Wisdom of Crowds by Minimax Entropy

    Dengyong Zhou;Sumit Basu;Yi Mao;John C. Platt

  • Spectral methods meet EM: a provably optimal algorithm for crowdsourcing

    Yuchen Zhang;Xi Chen;Dengyong Zhou;Michael I. Jordan

  • Semi-supervised protein classification using cluster kernels

    Jason Weston;Christina Leslie;Eugene Ie;Dengyong Zhou

  • Semi-supervised Protein Classification Using Cluster Kernels

    Jason Weston;Dengyong Zhou;André Elisseeff;William S. Noble

  • A Regularization Framework for Learning from Graph Data

    D Zhou;B Schölkopf

  • Semi-supervised Learning on Directed Graphs

    Dengyong Zhou;Thomas Hofmann;Bernhard Schölkopf

  • Neuro-Symbolic Program Synthesis

    Emilio Parisotto;Abdel-rahman Mohamed;Rishabh Singh;Lihong Li

  • Regularization on discrete spaces

    Dengyong Zhou;Bernhard Schölkopf

  • On evolutionary spectral clustering

    Yun Chi;Xiaodan Song;Dengyong Zhou;Koji Hino

  • Breaking the curse of horizon: Infinite-horizon off-policy estimation

    Qiang Liu;Lihong Li;Ziyang Tang;Dengyong Zhou

  • Protein ranking: from local to global structure in the protein similarity network.

    Jason Weston;Andre Elisseeff;Dengyong Zhou;Christina S. Leslie

  • Learning from Labeled and Unlabeled Data Using Random Walks

    Dengyong Zhou;Bernhard Schölkopf

Frequent Co-Authors

Lihong Li
Lihong Li Amazon (United States)
Nihar B. Shah
Nihar B. Shah Carnegie Mellon University
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Xi Chen
Xi Chen Columbia University
Christopher J. C. Burges
Christopher J. C. Burges Microsoft (United States)
Jason Weston
Jason Weston Facebook (United States)
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Li Deng
Li Deng Citadel
Lin Xiao
Lin Xiao Facebook (United States)
Christina S. Leslie
Christina S. Leslie Memorial Sloan Kettering Cancer Center

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