Martin Takáč
Martin Takáč
Mohamed Bin Zayed University of Artificial Intelligence
Verified email at lehigh.edu - Homepage
Title
Cited by
Cited by
Year
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P Richtárik, M Takáč
Mathematical Programming 144 (1), 1-38, 2014
7112014
Parallel coordinate descent methods for big data optimization
P Richtárik, M Takáč
Mathematical Programming, Series A, 1-52, 2015
4772015
Reinforcement learning for solving the vehicle routing problem
M Nazari, A Oroojlooy, LV Snyder, M Takáč
arXiv preprint arXiv:1802.04240, 2018
3472018
Communication-efficient distributed dual coordinate ascent
M Jaggi, V Smith, M Takác, J Terhorst, S Krishnan, T Hofmann, MI Jordan
Advances in neural information processing systems, 3068-3076, 2014
3382014
SARAH: A novel method for machine learning problems using stochastic recursive gradient
L Nguyen, J Liu, K Scheinberg, M Takáč
In 34th International Conference on Machine Learning, ICML 2017, 2017
3292017
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
2512015
Distributed coordinate descent method for learning with big data
P Richtárik, M Takác
Journal of Machine Learning Research 17, 1-25, 2016
2122016
Mini-batch primal and dual methods for SVMs
M Takáč, A Bijral, P Richtárik, N Srebro
In 30th International Conference on Machine Learning, ICML 2013, 2013
197*2013
CoCoA: A general framework for communication-efficient distributed optimization
V Smith, S Forte, M Chenxin, M Takáč, MI Jordan, M Jaggi
Journal of Machine Learning Research 18, 230, 2018
1752018
Adding vs. averaging in distributed primal-dual optimization
C Ma, V Smith, M Jaggi, MI Jordan, P Richtárik, M Takáč
In 32nd International Conference on Machine Learning, ICML 2015, 2015
1732015
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takáč
In 34th International Conference on Machine Learning, ICML 2018, 2018
1212018
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, 2017
1172017
On optimal probabilities in stochastic coordinate descent methods
P Richtárik, M Takáč
Optimization Letters, 2015, 1-11, 2015
1102015
SDNA: stochastic dual newton ascent for empirical risk minimization
Z Qu, P Richtárik, M Takáč, O Fercoq
In 33rd International Conference on Machine Learning, ICML 2016, 2016
882016
A Multi-Batch L-BFGS Method for Machine Learning
AS Berahas, J Nocedal, M Takáč
The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), 2016
832016
Distributed learning with compressed gradient differences
K Mishchenko, E Gorbunov, M Takáč, P Richtárik
arXiv preprint arXiv:1901.09269, 2019
782019
Stochastic recursive gradient algorithm for nonconvex optimization
LM Nguyen, J Liu, K Scheinberg, M Takáč
arXiv preprint arXiv:1705.07261, 2017
702017
Fast distributed coordinate descent for non-strongly convex losses
O Fercoq, Z Qu, P Richtárik, M Takáč
IEEE Workshop on Machine Learning for Signal Processing, 2014, 2014
662014
Stochastic reformulations of linear systems: algorithms and convergence theory
P Richtárik, M Takác
SIAM Journal on Matrix Analysis and Applications 41 (2), 487-524, 2020
622020
Applying deep learning to the newsvendor problem
A Oroojlooyjadid, LV Snyder, M Takáč
IISE Transactions 52 (4), 444-463, 2020
592020
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