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Computer Science

D-Index
82
Citations
34866
World Ranking
943
National Ranking
515

Overview

Mehryar Mohri is affiliated with Google in the United States and has a substantial body of research primarily in the field of Computer Science. Their work spans multiple subfields including Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computer Vision and Pattern Recognition, and Electrical and Electronic Engineering.

The research topics covered by Mehryar Mohri encompass areas such as Machine Learning and Algorithms, Advanced Bandit Algorithms Research, Domain Adaptation and Few-Shot Learning, Adversarial Robustness in Machine Learning, Machine Learning and Data Classification, Reinforcement Learning in Robotics, and Privacy-Preserving Technologies in Data.

Recent publications by Mehryar Mohri include the following papers:

  • Advances and Open Problems in Federated Learning, 2020, published in Foundations and Trends® in Machine Learning
  • Three Approaches for Personalization with Applications to Federated Learning, 2020, arXiv (Cornell University)
  • Cross-Entropy Loss Functions: Theoretical Analysis and Applications, 2023, arXiv (Cornell University)
  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)
  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning, 2020, arXiv (Cornell University)

Mehryar Mohri has collaborated frequently with several researchers, including Yutao Zhong, Pranjal Awasthi, Anqi Mao, Corinna Cortes, and Yishay Mansour. These collaborations have contributed to a consistent output of research, particularly reflected in multiple publications on arXiv (Cornell University).

Their frequent publication venues emphasize their connection to both preprint and peer-reviewed outlets, with a strong presence on arXiv (Cornell University), where they have 51 publications. Other venues include the Annals of Mathematics and Artificial Intelligence with 5 publications, Foundations and Trends® in Machine Learning, and the Journal of Global Optimization.

Best Publications

  • Advances and Open Problems in Federated Learning

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

  • Foundations of Machine Learning

    Mehryar Mohri;Afshin Rostamizadeh;Afshin Rostamizadeh;Ameet Talwalkar;Ameet Talwalkar

  • Finite-state transducers in language and speech processing

    Mehryar Mohri

  • Weighted finite-state transducers in speech recognition

    Mehryar Mohri;Fernando Pereira;Michael Riley

  • Advances and open problems in federated learning

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

  • SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

    Sai Praneeth Reddy Karimireddy;Satyen Kale;Mehryar Mohri;Sashank Jakkam Reddi

  • OpenFst: a general and efficient weighted finite-state transducer library

    Cyril Allauzen;Michael Riley;Johan Schalkwyk;Wojciech Skut

  • AUC Optimization vs. Error Rate Minimization

    Corinna Cortes;Mehryar Mohri

  • Multi-armed bandit algorithms and empirical evaluation

    Joannès Vermorel;Mehryar Mohri

  • Domain adaptation: Learning bounds and algorithms

    Yishay Mansour;Mehryar Mohri;Afshin Rostamizadeh

  • Algorithms for learning kernels based on centered alignment

    Corinna Cortes;Mehryar Mohri;Afshin Rostamizadeh

  • Domain Adaptation with Multiple Sources

    Yishay Mansour;Mehryar Mohri;Afshin Rostamizadeh

  • Agnostic federated learning

    Mehryar Mohri;Gary Sivek;Ananda Theertha Suresh

  • L 2 regularization for learning kernels

    Corinna Cortes;Mehryar Mohri;Afshin Rostamizadeh

  • Speech Recognition with Weighted Finite-State Transducers

    Mehryar Mohri;Fernando C. N. Pereira;Michael Riley

  • Semiring frameworks and algorithms for shortest-distance problems

    Mehryar Mohri

  • Sample Selection Bias Correction Theory

    Corinna Cortes;Mehryar Mohri;Michael Riley;Afshin Rostamizadeh

  • Sampling methods for the Nyström method

    Sanjiv Kumar;Mehryar Mohri;Ameet Talwalkar

  • Methods and Apparatus for Rapid Acoustic Unit Selection From a Large Speech Corpus

    Mark Charles Beutnagel;Mehryar Mohri;Michael Dennis Riley

  • Three Approaches for Personalization with Applications to Federated Learning.

    Yishay Mansour;Mehryar Mohri;Jae Ro;Ananda Theertha Suresh

Frequent Co-Authors

Corinna Cortes
Corinna Cortes Google (United States)
Michael Riley
Michael Riley Google (United States)
Afshin Rostamizadeh
Afshin Rostamizadeh Google (United States)
Ameet Talwalkar
Ameet Talwalkar Carnegie Mellon University
Satyen Kale
Satyen Kale Google (United States)
Yishay Mansour
Yishay Mansour Tel Aviv University
Fernando Pereira
Fernando Pereira Google (United States)
Karthik Sridharan
Karthik Sridharan Cornell University
Patrick Haffner
Patrick Haffner Interactions Corporation
Brian Roark
Brian Roark Google (United States)

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