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Jakub Konečný
Jakub Konečný
Research Scientist, Google
Zweryfikowany adres z google.com
Tytuł
Cytowane przez
Cytowane przez
Rok
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and Trends® in Machine Learning 14, 1-210, 2019
66782019
Federated Learning: Strategies for Improving Communication Efficiency
J Konečný
arXiv preprint arXiv:1610.05492, 2016
57552016
Towards federated learning at scale: System design
K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ...
Proceedings of the 2nd SysML Conference, 2019
33782019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
23982016
Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
International Conference on Learning Representations, 2020
15272020
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ...
arXiv preprint arXiv:1812.01097, 2018
15232018
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
8352015
Improving federated learning personalization via model agnostic meta learning
Y Jiang, J Konečný, K Rush, S Kannan
arXiv preprint arXiv:1909.12488, 2019
6662019
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
5142018
A field guide to federated optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
3812021
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
3392015
Semi-stochastic gradient descent methods
J Konečný, P Richtárik
Frontiers in Applied Mathematics and Statistics 3, 2017
2772017
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
2322015
Aide: Fast and communication efficient distributed optimization
SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola
arXiv preprint arXiv:1608.06879, 2016
1802016
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
165*2015
One-Shot-Learning Gesture Recognition using HOG-HOF Features
J Konečný, M Hagara
Journal of Machine Learning Research 15, 2513-2532, 2013
1082013
Randomized distributed mean estimation: Accuracy vs. communication
J Konečný, P Richtárik
Frontiers in Applied Mathematics and Statistics 4, 62, 2018
1072018
Semi-stochastic coordinate descent
J Konečný, Z Qu, P Richtárik
Optimization Methods and Software 32 (5), 993-1005, 2017
1012017
Federated learning with autotuned communication-efficient secure aggregation
K Bonawitz, F Salehi, J Konečný, B McMahan, M Gruteser
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1222-1226, 2019
912019
Convergence and accuracy trade-offs in federated learning and meta-learning
Z Charles, J Konečný
International Conference on Artificial Intelligence and Statistics, 2575-2583, 2021
752021
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