Filip Hanzely
Filip Hanzely
PhD student, KAUST
Verified email at kaust.edu.sa - Homepage
TitleCited byYear
Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization
R Gower, F Hanzely, P Richtárik, SU Stich
Advances in Neural Information Processing Systems, 1619-1629, 2018
152018
Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
F Hanzely, P Richtarik, L Xiao
arXiv preprint arXiv:1808.03045, 2018
132018
Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
F Hanzely, P Richtárik
arXiv preprint arXiv:1809.09354, 2018
102018
Privacy preserving randomized gossip algorithms
F Hanzely, J Konečný, N Loizou, P Richtárik, D Grishchenko
arXiv preprint arXiv:1706.07636, 2017
92017
Fastest rates for stochastic mirror descent methods
F Hanzely, P Richtárik
arXiv preprint arXiv:1803.07374, 2018
82018
Testing for causality in reconstructed state spaces by an optimized mixed prediction method
A Krakovská, F Hanzely
Physical Review E 94 (5), 052203, 2016
82016
SEGA: Variance reduction via gradient sketching
F Hanzely, K Mishchenko, P Richtárik
Advances in Neural Information Processing Systems, 2082-2093, 2018
52018
A nonconvex projection method for robust pca
A Dutta, F Hanzely, P Richtárik
Proceedings of the AAAI Conference on Artificial Intelligence 33, 1468-1476, 2019
42019
99% of parallel optimization is inevitably a waste of time
K Mishchenko, F Hanzely, P Richtárik
arXiv preprint arXiv:1901.09437, 2019
32019
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
22019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods
F Hanzely, P Richtárik
arXiv preprint arXiv:1905.11266, 2019
12019
Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit
A Dutta, F Hanzely, J Liang, P Richtárik
arXiv preprint arXiv:1905.10598, 2019
12019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
E Gorbunov, F Hanzely, P Richtárik
arXiv preprint arXiv:1905.11261, 2019
2019
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