Jakub Konečný
Jakub Konečný
Research Scientist, Google
Verified email at google.com - Homepage
TitleCited byYear
Federated learning: Strategies for improving communication efficiency
J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon
arXiv preprint arXiv:1610.05492, 2016
Semi-stochastic gradient descent methods
J Konečný, P Richtárik
Frontiers in Applied Mathematics and Statistics 3, 2017
Mini-batch semi-stochastic gradient descent in the proximal setting
J Konečný, J Liu, P Richtárik, M Takáč
IEEE Journal of Selected Topics in Signal Processing 10 (2), 242-255, 2015
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
Stop Wasting My Gradients: Practical SVRG
R Harikandeh, MO Ahmed, A Virani, M Schmidt, J Konečný, S Sallinen
Advances in Neural Information Processing Systems, 2242-2250, 2015
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
One-Shot-Learning Gesture Recognition using HOG-HOF Features
J Konečný, M Hagara
Journal of Machine Learning Research 15, 2513-2532, 2013
Aide: Fast and communication efficient distributed optimization
SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola
arXiv preprint arXiv:1608.06879, 2016
Distributed optimization with arbitrary local solvers
C Ma, J Konečný, M Jaggi, V Smith, MI Jordan, P Richtárik, M Takáč
Optimization Methods and Software 32 (4), 813-848, 2015
Towards federated learning at scale: System design
K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ...
arXiv preprint arXiv:1902.01046, 2019
Semi-stochastic coordinate descent
J Konečný, Z Qu, P Richtárik
optimization Methods and Software 32 (5), 993-1005, 2017
Simple complexity analysis of simplified direct search
J Konečný, P Richtárik
arXiv preprint arXiv:1410.0390, 2014
Randomized distributed mean estimation: Accuracy vs communication
J Konečný, P Richtárik
Frontiers in Applied Mathematics and Statistics 4, 62, 2018
Leaf: A benchmark for federated settings
S Caldas, P Wu, T Li, J Konečný, HB McMahan, V Smith, A Talwalkar
arXiv preprint arXiv:1812.01097, 2018
Stochastic, Distributed and Federated Optimization for Machine Learning
J Konečný
arXiv preprint arXiv:1707.01155, 2017
Privacy preserving randomized gossip algorithms
F Hanzely, J Konečný, N Loizou, P Richtárik, D Grishchenko
arXiv preprint arXiv:1706.07636, 2017
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
S Caldas, J Konečný, HB McMahan, A Talwalkar
arXiv preprint arXiv:1812.07210, 2018
SysML: The New Frontier of Machine Learning Systems
A Ratner, D Alistarh, G Alonso, P Bailis, S Bird, N Carlini, B Catanzaro, ...
arXiv preprint arXiv:1904.03257, 2019
A privacy preserving randomized gossip algorithm via controlled noise insertion
F Hanzely, J Konečný, N Loizou, P Richtárik, D Grishchenko
arXiv preprint arXiv:1901.09367, 2019
Systems and Methods of Distributed Optimization
HB McMahan, J Konecny, EB Moore, D Ramage, BH Aguera-Arcas
US Patent App. 15/045,707, 2017
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