World's Best Scientists 2026 revealed!

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Computer Science

D-Index
47
Citations
11840
World Ranking
6374
National Ranking
2844

Overview

Satyen Kale is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a focus on artificial intelligence and related subfields. Their scholarly output includes research on stochastic gradient optimization techniques, privacy-preserving technologies in data, and advanced bandit algorithms.

Their research spans several specialized areas within computer science, including:

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Management Science and Operations Research
  • Computer Networks and Communications
  • Computational Mechanics

Kale's recent work involves topics such as:

  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Advanced Bandit Algorithms Research
  • Complexity and Algorithms in Graphs
  • Sparse and Compressive Sensing Techniques
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning

The researcher has published primarily in the venue arXiv (Cornell University), with 27 contributions noted there. Selected recent publications include:

  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)
  • Estimating Training Data Influence by Tracing Gradient Descent, 2020, arXiv (Cornell University)
  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning, 2020, arXiv (Cornell University)
  • On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data, 2022, arXiv (Cornell University)
  • On the Convergence of Federated Averaging with Cyclic Client Participation, 2023, arXiv (Cornell University)

Satyen Kale has collaborated frequently with a group of coauthors, with multiple joint publications. Notable coauthors include:

  • Naman Agarwal
  • Pranjal Awasthi
  • Gauri Joshi
  • Mehryar Mohri
  • Sashank J. Reddi

The body of work produced by Kale reflects a consistent engagement with topics critical to federated learning, optimization algorithms, and data privacy, contributing to both theoretical and applied dimensions within computer science research.

Best Publications

  • On the Convergence of Adam and Beyond

    Sashank J. Reddi;Satyen Kale;Sanjiv Kumar

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Amit Agarwal;Satyen Kale

  • The Multiplicative Weights Update Method: A Meta-Algorithm and Applications

    Sanjeev Arora;Elad Hazan;Satyen Kale

  • SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

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

  • Privacy, accuracy, and consistency too: a holistic solution to contingency table release

    Boaz Barak;Kamalika Chaudhuri;Cynthia Dwork;Satyen Kale

  • Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

    Alekh Agarwal;Daniel Hsu;Satyen Kale;John Langford

  • A Combinatorial, Primal-Dual Approach to Semidefinite Programs

    Sanjeev Arora;Satyen Kale

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Adam Kalai;Satyen Kale;Amit Agarwal

  • Algorithms for portfolio management based on the Newton method

    Amit Agarwal;Elad Hazan;Satyen Kale;Robert E. Schapire

  • Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization

    Elad Hazan;Satyen Kale

  • Adaptive Methods for Nonconvex Optimization

    Manzil Zaheer;Sashank J. Reddi;Devendra Singh Sachan;Satyen Kale

  • Efficient optimal learning for contextual bandits

    Miroslav Dudik;Daniel Hsu;Satyen Kale;Nikos Karampatziakis

  • SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.

    Sai Praneeth Karimireddy;Satyen Kale;Mehryar Mohri;Sashank J. Reddi

  • Who moderates the moderators?: crowdsourcing abuse detection in user-generated content

    Arpita Ghosh;Satyen Kale;Preston McAfee

  • Extracting certainty from uncertainty: regret bounded by variation in costs

    Elad Hazan;Satyen Kale

  • A Field Guide to Federated Optimization

    Jianyu Wang;Zachary Charles;Zheng Xu;Gauri Joshi

  • Fast algorithms for approximate semidefinite programming using the multiplicative weights update method

    S. Arora;E. Hazan;S. Kale

  • Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

    Elad Hazan;Satyen Kale

  • Projection-free Online Learning

    Elad Hazan;Satyen Kale

  • Efficient aggregation algorithms for probabilistic data

    T. S. Jayram;Satyen Kale;Erik Vee

  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

    Sai Praneeth Karimireddy;Martin Jaggi;Satyen Kale;Mehryar Mohri

  • Estimating Training Data Influence by Tracing Gradient Descent

    Garima Pruthi;Frederick Liu;Satyen Kale;Mukund Sundararajan

Frequent Co-Authors

Elad Hazan
Elad Hazan Princeton University
Sashank J. Reddi
Sashank J. Reddi Google (United States)
Mehryar Mohri
Mehryar Mohri Google (United States)
Sanjiv Kumar
Sanjiv Kumar Google (United States)
Sanjeev Arora
Sanjeev Arora Princeton University
C. Seshadhri
C. Seshadhri University of California, Santa Cruz
John Langford
John Langford Microsoft (United States)
Karthik Sridharan
Karthik Sridharan Cornell University
Robert E. Schapire
Robert E. Schapire Microsoft (United States)
Daniel Hsu
Daniel Hsu Columbia University

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