Hugh Brendan Mcmahan

Hugh Brendan Mcmahan's AcademicInfluence.com Rankings

Computer Science

#10078

World Rank

#10571

Historical Rank

Algorithms

#413

World Rank

#418

Historical Rank

Machine Learning

#4584

World Rank

#4635

Historical Rank

Database

#7034

World Rank

#7278

Historical Rank

computer-science Degrees

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Computer Science

Hugh Brendan Mcmahan's Degrees

PhD Computer Science Stanford University
Masters Computer Science Stanford University
Bachelors Computer Science Stanford University

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Hugh Brendan Mcmahan's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Communication-Efficient Learning of Deep Networks from Decentralized Data (2016) (7210)
Deep Learning with Differential Privacy (2016) (3519)
Federated Learning: Strategies for Improving Communication Efficiency (2016) (2898)
Advances and Open Problems in Federated Learning (2019) (2826)
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017) (1674)
Towards Federated Learning at Scale: System Design (2019) (1532)
Federated Optimization: Distributed Machine Learning for On-Device Intelligence (2016) (1235)
Ad click prediction: a view from the trenches (2013) (867)
Learning Differentially Private Recurrent Language Models (2017) (783)
LEAF: A Benchmark for Federated Settings (2018) (737)
Online convex optimization in the bandit setting: gradient descent without a gradient (2004) (695)
Federated Learning of Deep Networks using Model Averaging (2016) (656)
Adaptive Federated Optimization (2020) (587)
Federated Optimization: Distributed Optimization Beyond the Datacenter (2015) (494)
cpSGD: Communication-efficient and differentially-private distributed SGD (2018) (322)
Adaptive Bound Optimization for Online Convex Optimization (2010) (304)
Planning in the Presence of Cost Functions Controlled by an Adversary (2003) (290)
Robust Submodular Observation Selection (2008) (287)
Practical Secure Aggregation for Federated Learning on User-Held Data (2016) (277)
Can You Really Backdoor Federated Learning? (2019) (261)
Distributed Mean Estimation with Limited Communication (2016) (255)
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements (2018) (251)
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization (2011) (206)
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary (2004) (199)
A Field Guide to Federated Optimization (2021) (195)
Differentially Private Learning with Adaptive Clipping (2019) (172)
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees (2005) (168)
Is Local SGD Better than Minibatch SGD? (2020) (155)
A General Approach to Adding Differential Privacy to Iterative Training Procedures (2018) (134)
A survey of Algorithms and Analysis for Adaptive Online Learning (2014) (132)
Generative Models for Effective ML on Private, Decentralized Datasets (2019) (123)
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning (2014) (111)
Estimation, Optimization, and Parallelism when Data is Sparse (2013) (100)
Learning Differentially Private Language Models Without Losing Accuracy (2017) (96)
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization (2018) (93)
Tighter Bounds for Multi-Armed Bandits with Expert Advice (2009) (70)
Federated Heavy Hitters Discovery with Differential Privacy (2019) (68)
No-Regret Algorithms for Unconstrained Online Convex Optimization (2012) (62)
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations (2014) (59)
Semi-Cyclic Stochastic Gradient Descent (2019) (59)
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards (2009) (58)
Training Production Language Models without Memorizing User Data (2020) (54)
Federated Learning with Autotuned Communication-Efficient Secure Aggregation (2019) (52)
Fast Exact Planning in Markov Decision Processes (2005) (47)
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches (2017) (46)
Selecting Observations against Adversarial Objectives (2007) (46)
Privacy Amplification via Random Check-Ins (2020) (44)
Minimax Optimal Algorithms for Unconstrained Linear Optimization (2013) (37)
Less Regret via Online Conditioning (2010) (33)
A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates (2010) (29)
Open Problem: Better Bounds for Online Logistic Regression (2012) (24)
Large-Scale Learning with Less RAM via Randomization (2013) (23)
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games (2007) (23)
MLSys: The New Frontier of Machine Learning Systems (2019) (21)
SysML: The New Frontier of Machine Learning Systems (2019) (20)
Analysis Techniques for Adaptive Online Learning (2014) (17)
Federated Learning and Privacy (2021) (17)
Generalizing Dijkstra's Algorithm and Gaussian Elimination for Solving MDPs (2005) (13)
Robust planning in domains with stochastic outcomes, adversaries, and partial observability (2006) (11)
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams (2022) (10)
Multi-source spanning trees: algorithms for minimizing source eccentricities (2004) (9)
Online convex optimization in the bandit setting (2005) (9)
Efficiently computing minimax expected-size confidence regions (2007) (9)
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning (2022) (6)
On Large-Cohort Training for Federated Learning (2021) (5)
On Large-Cohort Training for Federated Learning (2021) (5)
Learning to Generate Image Embeddings with User-level Differential Privacy (2022) (5)
A ug 2 01 7 On the Protection of Private Information in Machine Learning Systems : Two Recent Approaches ( Invited Paper ) (2018) (4)
Planning in Cost-Paired Markov Decision Process Games (2003) (4)
A Unification of Extensive-Form Games and Markov Decision Processes (2007) (3)
Private Online Prefix Sums via Optimal Matrix Factorizations (2022) (3)
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and Implicit Updates (2010) (3)
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy (2023) (3)
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning (2022) (2)
Federated learning and privacy (2022) (1)
On Calibrated Predictions for Auction Selection Mechanisms (2012) (1)
Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees" (2011) (1)
T OWARDS F EDERATED L EARNING AT S CALE : S YSTEM D ESIGN (2022) (0)
Sensor Placement for Outbreak Detection in Computer Security (2007) (0)
One-shot Empirical Privacy Estimation for Federated Learning (2023) (0)
E XPANDING THE R EACH OF F EDERATED L EARNING BY R EDUCING C LIENT R ESOURCE R EQUIREMENTS (2021) (0)
An Empirical Evaluation of Federated Contextual Bandit Algorithms (2023) (0)
PRIVATE, DECENTRALIZED DATASETS (2020) (0)
Differentially Private Adaptive Optimization with Delayed Preconditioners (2022) (0)
Fast Exact Planning in Markov Decision Processes DRAFT — please check for updates before redistributing (0)

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Hugh Brendan Mcmahan's Academic­Influence.com Rankings

Hugh Brendan Mcmahan's Degrees

Similar Degrees You Can Earn

Why Is Hugh Brendan Mcmahan Influential?

Hugh Brendan Mcmahan's Published Works

Published Works

Hugh Brendan Mcmahan's AcademicInfluence.com Rankings