Martin Takáč
タイトル引用先
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P Richtárik, M Takáč
Mathematical Programming 144 (1-2), 1-38, 2014
5272014
Parallel coordinate descent methods for big data optimization
P Richtárik, M Takáč
Mathematical Programming, Series A, 1-52, 2015
3652015
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
1982014
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
148*2015
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
148*2013
Distributed coordinate descent method for learning with big data
P Richtárik, M Takác
Journal of Machine Learning Research 17, 1-25, 2016
1432016
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
932015
On optimal probabilities in stochastic coordinate descent methods
P Richtárik, M Takáč
Optimization Letters, 2015, 1-11, 2015
762015
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
732017
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
592016
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
532014
Distributed block coordinate descent for minimizing partially separable functions
J Marecek, P Richtárik, M Takac
Numerical Analysis and Optimization 2014, Springer Proceedings in …, 2014
502014
Efficient serial and parallel coordinate descent methods for huge-scale truss topology design
P Richtárik, M Takáč
Operations Research Proceedings 2011, 27-32, 2012
492012
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
472018
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
382017
Primal-Dual Rates and Certificates
C Dünner, S Forte, M Takáč, M Jaggi
In 33rd International Conference on Machine Learning, ICML 2016, 2016
372016
Distributed mini-batch SDCA
M Takáč, P Richtárik, N Srebro
arXiv preprint arXiv:1507.08322, 2015
352015
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
322016
Alternating maximization: Unifying framework for 8 sparse PCA formulations and efficient parallel codes
P Richtárik, M Takáč, SD Ahipaşaoğlu
arXiv preprint arXiv:1212.4137, 2012
302012
On the complexity of parallel coordinate descent
R Tappenden, M Takáč, P Richtárik
Optimization Methods and Software 33 (2), 372-395, 2018
292018
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