Zachary Charles | DeepAI

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∙ 02/02/2023

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning

We study stochastic optimization with linearly correlated noise. Our stu...

0 Anastasia Koloskova, et al. ∙

∙ 01/18/2023

Federated Automatic Differentiation

Federated learning (FL) is a general framework for learning across heter...

0 Keith Rush, et al. ∙

∙ 08/19/2022

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

Federated learning (FL) is a framework for machine learning across heter...

6 Zachary Charles, et al. ∙

∙ 06/18/2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Personalized federated learning considers learning models unique to each...

0 Shanshan Wu, et al. ∙

∙ 01/07/2022

Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory

A significant bottleneck in federated learning is the network communicat...

14 Nicole Mitchell, et al. ∙

∙ 09/08/2021

Iterated Vector Fields and Conservatism, with Applications to Federated Learning

We study iterated vector fields and investigate whether they are conserv...

0 Zachary Charles, et al. ∙

∙ 06/15/2021

On Large-Cohort Training for Federated Learning

Federated learning methods typically learn a model by iteratively sampli...

0 Zachary Charles, et al. ∙

∙ 06/04/2021

Local Adaptivity in Federated Learning: Convergence and Consistency

The federated learning (FL) framework trains a machine learning model us...

0 Jianyu Wang, et al. ∙

∙ 03/08/2021

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning

We study a family of algorithms, which we refer to as local update metho...

0 Zachary Charles, et al. ∙

∙ 07/02/2020

On the Outsized Importance of Learning Rates in Local Update Methods

We study a family of algorithms, which we refer to as local update metho...

0 Zachary Charles, et al. ∙

∙ 02/29/2020

Adaptive Federated Optimization

Federated learning is a distributed machine learning paradigm in which a...

28 Sashank Reddi, et al. ∙

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

∙ 10/18/2019

Improving the convergence of SGD through adaptive batch sizes

Mini-batch stochastic gradient descent (SGD) approximates the gradient o...

0 Scott Sievert, et al. ∙

∙ 07/29/2019

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

To improve the resilience of distributed training to worst-case, or Byza...

0 Shashank Rajput, et al. ∙

∙ 05/22/2019

Convergence and Margin of Adversarial Training on Separable Data

Adversarial training is a technique for training robust machine learning...

0 Zachary Charles, et al. ∙

∙ 05/08/2019

Does Data Augmentation Lead to Positive Margin?

Data augmentation (DA) is commonly used during model training, as it sig...

0 Shashank Rajput, et al. ∙

∙ 01/28/2019

ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding

We present ErasureHead, a new approach for distributed gradient descent ...

0 Hongyi Wang, et al. ∙

∙ 11/08/2018

A Geometric Perspective on the Transferability of Adversarial Directions

State-of-the-art machine learning models frequently misclassify inputs t...

0 Zachary Charles, et al. ∙

∙ 06/11/2018

ATOMO: Communication-efficient Learning via Atomic Sparsification

Distributed model training suffers from communication overheads due to f...

0 Hongyi Wang, et al. ∙

∙ 05/25/2018

Gradient Coding via the Stochastic Block Model

Gradient descent and its many variants, including mini-batch stochastic ...

0 Zachary Charles, et al. ∙

∙ 03/27/2018

DRACO: Robust Distributed Training via Redundant Gradients

Distributed model training is vulnerable to worst-case system failures a...

0 Lingjiao Chen, et al. ∙

∙ 11/17/2017

Approximate Gradient Coding via Sparse Random Graphs

Distributed algorithms are often beset by the straggler effect, where th...

0 Zachary Charles, et al. ∙

∙ 10/23/2017

Stability and Generalization of Learning Algorithms that Converge to Global Optima

We establish novel generalization bounds for learning algorithms that co...

0 Zachary Charles, et al. ∙

∙ 07/08/2017

Subspace Clustering with Missing and Corrupted Data

Subspace clustering is the process of identifying a union of subspaces m...

0 Zachary Charles, et al. ∙