Jakub Konecný

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 36

Top Topics

Machine Learning

Spatially Distributed

Top Venues

Optim. Methods Softw.

Frontiers Appl. Math. Stat.

Publications

Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
A Rate-Distortion View on Model Updates. Tiny Papers @ ICLR (2023)
Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR (2022)
Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. CoRR (2022)
Zachary Charles, Jakub Konecný
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. AISTATS (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Zachary Charles, Jakub Konecný
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. CoRR (2021)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan
Adaptive Federated Optimization. ICLR (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Zachary Charles, Jakub Konecný
On the Outsized Importance of Learning Rates in Local Update Methods. CoRR (2020)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan
Adaptive Federated Optimization. CoRR (2020)
Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. CoRR (2019)
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Towards Federated Learning at Scale: System Design. MLSys (2019)
Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR (2019)
Yihan Jiang, Jakub Konecný, Keith Rush, Sreeram Kannan
Improving Federated Learning Personalization via Model Agnostic Meta Learning. CoRR (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. ACSSC (2019)
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Towards Federated Learning at Scale: System Design. CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. CoRR (2018)
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
LEAF: A Benchmark for Federated Settings. CoRR (2018)
Jakub Konecný, Peter Richtárik
Randomized Distributed Mean Estimation: Accuracy vs. Communication. Frontiers Appl. Math. Stat. 4 (2018)
Jakub Konecný, Peter Richtárik
Semi-Stochastic Gradient Descent Methods. Frontiers Appl. Math. Stat. 3 (2017)
Lukas Malina, Jakub Konecný, Petr Dzurenda, Jan Hajny
On practical deployment of smart card based authenticated key agreement schemes. ICUMT (2017)
Jakub Konecný
Stochastic, Distributed and Federated Optimization for Machine Learning. CoRR (2017)
Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32 (4) (2017)
Jakub Konecný, Zheng Qu, Peter Richtárik
Semi-stochastic coordinate descent. Optim. Methods Softw. 32 (5) (2017)
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR (2016)
Jakub Konecný, Peter Richtárik
Randomized Distributed Mean Estimation: Accuracy vs Communication. CoRR (2016)
Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. IEEE J. Sel. Top. Signal Process. 10 (2) (2016)
Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon
Federated Learning: Strategies for Improving Communication Efficiency. CoRR (2016)
Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola
AIDE: Fast and Communication Efficient Distributed Optimization. CoRR (2016)
Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
Distributed Optimization with Arbitrary Local Solvers. CoRR (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
Stop Wasting My Gradients: Practical SVRG. CoRR (2015)
Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
StopWasting My Gradients: Practical SVRG. NIPS (2015)
Jakub Konecný, Brendan McMahan, Daniel Ramage
Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR (2015)
Jakub Konecný, Zheng Qu, Peter Richtárik
Semi-Stochastic Coordinate Descent. CoRR (2014)
Jakub Konecný, Michal Hagara
One-shot-learning gesture recognition using HOG-HOF features. J. Mach. Learn. Res. 15 (1) (2014)
Jakub Konecný, Peter Richtárik
Simple Complexity Analysis of Direct Search. CoRR (2014)
Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR (2014)
Jakub Konecný, Michal Hagara
One-Shot-Learning Gesture Recognition using HOG-HOF Features. CoRR (2013)
Jakub Konecný, Peter Richtárik
Semi-Stochastic Gradient Descent Methods. CoRR (2013)