World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
68
Citations
29757
World Ranking
2032
National Ranking
1028

Mathematics

D-Index
68
Citations
29696
World Ranking
298
National Ranking
165

Research.com Recognitions

  • 2017 - Fellow of Alfred P. Sloan Foundation

Overview

John C. Duchi is affiliated with Stanford University in the United States and works primarily in the field of Computer Science. Their research spans several subfields, including Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Computational Mechanics, and Computer Networks and Communications.

The scientist's main research topics involve Statistical Methods and Inference, Stochastic Gradient Optimization Techniques, Privacy-Preserving Technologies in Data, Sparse and Compressive Sensing Techniques, Machine Learning and Data Classification, Adversarial Robustness in Machine Learning, and Machine Learning and Algorithms.

John C. Duchi has contributed to numerous publications, with many appearing in the following venues:

  • arXiv (Cornell University)
  • The Annals of Statistics
  • Mathematical Programming
  • Mathematics of Operations Research
  • Journal of the American Statistical Association

Their recent papers include:

  • Lower bounds for non-convex stochastic optimization, 2022, Mathematical Programming
  • Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach, 2021, Mathematics of Operations Research
  • Learning models with uniform performance via distributionally robust optimization, 2021, The Annals of Statistics
  • Understanding and Mitigating the Tradeoff Between Robustness and Accuracy, 2020, arXiv (Cornell University)

Frequent collaborators with John C. Duchi include Maxime Cauchois, Suyash Gupta, Hilal Asi, Hongseok Namkoong, and Alnur Ali.

In recognition of their work, John C. Duchi was named a Fellow of the Alfred P. Sloan Foundation in 2017.

Best Publications

  • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

    John Duchi;Elad Hazan;Yoram Singer

  • Efficient projections onto the l1-ball for learning in high dimensions

    John Duchi;Shai Shalev-Shwartz;Yoram Singer;Tushar Chandra

  • Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

    J. C. Duchi;A. Agarwal;M. J. Wainwright

  • Local privacy and statistical minimax rates

    John C. Duchi;Michael I. Jordan;Martin J. Wainwright

  • Communication-efficient algorithms for statistical optimization

    Yuchen Zhang;John C. Duchi;Martin J. Wainwright

  • Efficient Online and Batch Learning Using Forward Backward Splitting

    John Duchi;Yoram Singer

  • Distributed delayed stochastic optimization

    Alekh Agarwal;John C. Duchi

  • Certifying Some Distributional Robustness with Principled Adversarial Training

    Aman Sinha;Hongseok Namkoong;John C. Duchi

  • Generalizing to Unseen Domains via Adversarial Data Augmentation

    Riccardo Volpi;Hongseok Namkoong;Ozan Sener;John C. Duchi

  • Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations

    John C. Duchi;Michael I. Jordan;Martin J. Wainwright;Andre Wibisono

  • MLbase: A Distributed Machine-learning System

    Tim Kraska;Ameet Talwalkar;John C. Duchi;Rean Griffith

  • Unlabeled Data Improves Adversarial Robustness

    Yair Carmon;Aditi Raghunathan;Ludwig Schmidt;John C. Duchi

  • Minimax Optimal Procedures for Locally Private Estimation

    John C. Duchi;Michael I. Jordan;Martin J. Wainwright

  • Composite objective mirror descent

    John C. Duchi;Shai Shalev-Shwartz;Yoram Singer;Ambuj Tewari

  • Protection Against Reconstruction and Its Applications in Private Federated Learning

    Abhishek Bhowmick;John C. Duchi;Julien Freudiger;Gaurav Kapoor

  • Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates

    Yuchen Zhang;John Duchi;Martin Wainwright

  • Accelerated Methods for Non-Convex Optimization

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • Divide and Conquer Kernel Ridge Regression

    Yuchen Zhang;John C. Duchi;Martin J. Wainwright

  • Information-theoretic lower bounds for distributed statistical estimation with communication constraints

    Yuchen Zhang;John Duchi;Michael I Jordan;Martin J Wainwright

  • RANDOMIZED SMOOTHING FOR STOCHASTIC OPTIMIZATION

    John C. Duchi;Peter L. Bartlett;Martin J. Wainwright

  • Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

    Hongseok Namkoong;John C. Duchi

  • Variance-based Regularization with Convex Objectives

    Hongseok Namkoong;John C. Duchi

  • Lower Bounds for Non-Convex Stochastic Optimization.

    Yossi Arjevani;Yair Carmon;John C. Duchi;Dylan J. Foster

  • Minimax Optimal Procedures for Locally Private Estimation

    John Duchi;Martin Wainwright;Michael Jordan

Frequent Co-Authors

Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Alekh Agarwal
Alekh Agarwal Google (United States)
Percy Liang
Percy Liang Stanford University
Aaron Sidford
Aaron Sidford Stanford University
Yoram Singer
Yoram Singer Princeton University
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Mikael Johansson
Mikael Johansson Royal Institute of Technology
Daphne Koller
Daphne Koller insitro Inc.

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