World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
7690
World Ranking
12424
National Ranking
5037

Overview

Sashank J. Reddi is affiliated with Google in the United States and has contributed extensively to the field of computer science, particularly in artificial intelligence and machine learning. Their research output comprises a significant number of publications focusing on optimization techniques, privacy, and advanced neural application methods.

The scientist's recent publications include:

  • "A Field Guide to Federated Optimization", 2021, arXiv (Cornell University)
  • "Adaptive Federated Optimization", 2020, arXiv (Cornell University)
  • "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", 2020, arXiv (Cornell University)
  • "$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers", 2020, arXiv (Cornell University)
  • "Why distillation helps: a statistical perspective", 2020, arXiv (Cornell University)

Their frequent collaborators include:

  • Sanjiv Kumar
  • Ankit Singh Rawat
  • Aditya Krishna Menon
  • Nikunj Saunshi
  • Srinadh Bhojanapalli

Prominent publication venues for Sashank J. Reddi primarily include arXiv (Cornell University), evidencing a focus on accessible and cutting-edge research dissemination.

The main fields of study represented in their work are:

  • Computer Science

Their subfields of study encompass:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computational Mechanics
  • Information Systems
  • Signal Processing

Core topics emphasized in their research are:

  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Domain Adaptation and Few-Shot Learning
  • Sparse and Compressive Sensing Techniques
  • Machine Learning and Extreme Learning Machines (ELM)
  • Advanced Neural Network Applications
  • Text and Document Classification Technologies

Best Publications

  • On the Convergence of Adam and Beyond

    Sashank J. Reddi;Satyen Kale;Sanjiv Kumar

  • SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

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

  • Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

    Yang You;Jing Li;Sashank Reddi;Jonathan Hseu

  • Stochastic variance reduction for nonconvex optimization

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós

  • Adaptive Methods for Nonconvex Optimization

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

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

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

  • Adaptive Federated Optimization

    Sashank Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

  • Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alexander J. Smola

  • Riemannian SVRG: fast stochastic optimization on riemannian manifolds

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

  • A Field Guide to Federated Optimization

    Jianyu Wang;Zachary Charles;Zheng Xu;Gauri Joshi

  • On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions

    Aaditya Ramdas;Sashank J. Reddi;Barnabás Póczos;Aarti Singh

  • On variance reduction in stochastic gradient descent and its asynchronous variants

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Pöczos

  • Adaptive Federated Optimization

    Sashank J. Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

  • AIDE: Fast and Communication Efficient Distributed Optimization

    Sashank J. Reddi;Jakub Konecný;Peter Richtárik;Barnabás Póczos

  • Stochastic Frank-Wolfe methods for nonconvex optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alex Smola

  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

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

  • Are Transformers universal approximators of sequence-to-sequence functions?

    Chulhee Yun;Srinadh Bhojanapalli;Ankit Singh Rawat;Sashank Reddi

  • Why are Adaptive Methods Good for Attention Models

    Jingzhao Zhang;Sai Praneeth Karimireddy;Andreas Veit;Seungyeon Kim

  • Can gradient clipping mitigate label noise

    Aditya Krishna Menon;Ankit Singh Rawat;Sashank J. Reddi;Sanjiv Kumar

  • Variance Reduction in Stochastic Gradient Langevin Dynamics

    Kumar Avinava Dubey;Sashank J. Reddi;Sinead A. Williamson;Barnabas Poczos

  • AdaCliP: Adaptive Clipping for Private SGD.

    Venkatadheeraj Pichapati;Ananda Theertha Suresh;Felix X. Yu;Sashank J. Reddi

  • Fast stochastic optimization on Riemannian manifolds.

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

Frequent Co-Authors

Sanjiv Kumar
Sanjiv Kumar Google (United States)
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Satyen Kale
Satyen Kale Google (United States)
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Felix X. Yu
Felix X. Yu Google (United States)
Aarti Singh
Aarti Singh Carnegie Mellon University
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Mehryar Mohri
Mehryar Mohri Google (United States)

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