Jakub Konecný

According to our database1, Jakub Konecný authored at least 34 papers between 2014 and 2023.

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Bibliography

2023
A Rate-Distortion View on Model Updates.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory.
CoRR, 2022

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

A Field Guide to Federated Optimization.
CoRR, 2021

Adaptive Federated Optimization.
Proceedings of the 9th International Conference on Learning Representations, 2021

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
On the Outsized Importance of Learning Rates in Local Update Methods.
CoRR, 2020

2019
Advances and Open Problems in Federated Learning.
CoRR, 2019

Improving Federated Learning Personalization via Model Agnostic Meta Learning.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion.
CoRR, 2019

Towards Federated Learning at Scale: System Design.
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Federated Learning with Autotuned Communication-Efficient Secure Aggregation.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
Randomized Distributed Mean Estimation: Accuracy vs. Communication.
Frontiers Appl. Math. Stat., 2018

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.
CoRR, 2018

LEAF: A Benchmark for Federated Settings.
CoRR, 2018

2017
Stochastic, distributed and federated optimization for machine learning
PhD thesis, 2017

Distributed optimization with arbitrary local solvers.
Optim. Methods Softw., 2017

Semi-stochastic coordinate descent.
Optim. Methods Softw., 2017

Semi-Stochastic Gradient Descent Methods.
Frontiers Appl. Math. Stat., 2017

Stochastic, Distributed and Federated Optimization for Machine Learning.
CoRR, 2017

On practical deployment of smart card based authenticated key agreement schemes.
Proceedings of the 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, 2017

One-Shot-Learning Gesture Recognition Using HOG-HOF Features.
Proceedings of the Gesture Recognition, 2017

2016
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting.
IEEE J. Sel. Top. Signal Process., 2016

AIDE: Fast and Communication Efficient Distributed Optimization.
CoRR, 2016

Federated Learning: Strategies for Improving Communication Efficiency.
CoRR, 2016

Federated Optimization: Distributed Machine Learning for On-Device Intelligence.
CoRR, 2016

2015
Federated Optimization: Distributed Optimization Beyond the Datacenter.
CoRR, 2015

Stop Wasting My Gradients: Practical SVRG.
CoRR, 2015

StopWasting My Gradients: Practical SVRG.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
One-shot-learning gesture recognition using HOG-HOF features.
J. Mach. Learn. Res., 2014

Simple Complexity Analysis of Direct Search.
CoRR, 2014

mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting.
CoRR, 2014


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