World's Best Scientists 2026 revealed!

Overview

Felix X. Yu is affiliated with Google in the United States. Their research primarily falls within the field of Computer Science, with a significant focus on Artificial Intelligence. Additional subfields in their work include Signal Processing, Computer Science Applications, Information Systems, and Computational Theory and Mathematics.

The research topics that Felix X. Yu has contributed to cover a range of areas such as Privacy-Preserving Technologies in Data, Text and Document Classification Technologies, Machine Learning and Algorithms, Stochastic Gradient Optimization Techniques, Mobile Crowdsensing and Crowdsourcing, Topic Modeling, and Cryptography and Data Security.

Recent publications by Felix X. Yu or involving their authorship include:

  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)
  • Pre-training Tasks for Embedding-based Large-scale Retrieval, 2020, arXiv (Cornell University)
  • Federated Learning with Only Positive Labels, 2020, arXiv (Cornell University)
  • FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients, 2022, arXiv (Cornell University)
  • Doubly-stochastic mining for heterogeneous retrieval, 2020, arXiv (Cornell University)

The main venue for publication has been arXiv (Cornell University), where Felix X. Yu has published at least ten papers.

Frequent co-authors collaborating with Felix X. Yu include:

  • Sashank J. Reddi
  • Ankit Singh Rawat
  • Aditya Krishna Menon
  • Wittawat Jitkrittum
  • Sanjiv Kumar

Best Publications

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Federated Learning: Strategies for Improving Communication Efficiency

    Jakub Konečný;H. Brendan McMahan;Felix X. Yu;Peter Richtarik

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

    Yu Cheng;Yu Cheng;Felix X. Yu;Rogerio S. Feris;Sanjiv Kumar

  • cpSGD: communication-efficient and differentially-private distributed SGD

    Naman Agarwal;Ananda Theertha Suresh;Felix Yu;Sanjiv Kumar

  • Designing Category-Level Attributes for Discriminative Visual Recognition

    Felix X. Yu;Liangliang Cao;Rogerio S. Feris;John R. Smith

  • Self-supervised Learning for Large-scale Item Recommendations

    Tiansheng Yao;Xinyang Yi;Derek Zhiyuan Cheng;Felix Yu

  • Distributed mean estimation with limited communication

    Ananda Theertha Suresh;Felix X. Yu;Sanjiv Kumar;H. Brendan McMahan

  • Pre-training Tasks for Embedding-based Large-scale Retrieval

    Wei-Cheng Chang;Felix X. Yu;Yin-Wen Chang;Yiming Yang

  • Orthogonal Random Features

    Felix Xinnan X. Yu;Ananda Theertha Suresh;Krzysztof M. Choromanski;Daniel N. Holtmann-Rice

  • A Field Guide to Federated Optimization

    Jianyu Wang;Zachary Charles;Zheng Xu;Gauri Joshi

  • Circulant Binary Embedding

    Felix Yu;Sanjiv Kumar;Yunchao Gong;Shih-Fu Chang

  • Video Event Detection by Inferring Temporal Instance Labels

    Kuan-Ting Lai;Felix X. Yu;Ming-Syan Chen;Shih-Fu Chang

  • Learning Spread-Out Local Feature Descriptors

    Xu Zhang;Felix X. Yu;Sanjiv Kumar;Shih-Fu Chang

  • Deep Transfer Network: Unsupervised Domain Adaptation

    Xu Zhang;Felix Xinnan Yu;Shih-Fu Chang;Shengjin Wang

  • Object-Based Visual Sentiment Concept Analysis and Application

    Tao Chen;Felix X. Yu;Jiawei Chen;Yin Cui

  • Weak attributes for large-scale image retrieval

    Felix X. Yu;Rongrong Ji;Ming-Hen Tsai;Guangnan Ye

  • Learning Discriminative and Transformation Covariant Local Feature Detectors

    Xu Zhang;Felix X. Yu;Svebor Karaman;Shih-Fu Chang

  • Modifying Memories in Transformer Models

    Chen Zhu;Ankit Singh Rawat;Manzil Zaheer;Srinadh Bhojanapalli

  • Additional Remarks on Designing Category-Level Attributes for Discriminative Visual Recognition

    Felix X Yu;Liangliang Cao;Rogerio S Feris;John R Smith

Frequent Co-Authors

Sanjiv Kumar
Sanjiv Kumar Google (United States)
Shih-Fu Chang
Shih-Fu Chang Columbia University
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Sashank J. Reddi
Sashank J. Reddi Google (United States)
H. Brendan McMahan
H. Brendan McMahan Google (United States)
Yu Cheng
Yu Cheng Microsoft (United States)
Rogerio Feris
Rogerio Feris IBM (United States)
Liangliang Cao
Liangliang Cao Google (United States)
John R. Smith
John R. Smith IBM (United States)
Tony Jebara
Tony Jebara Columbia University

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