Takip et
Martin Takáč
Martin Takáč
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
mbzuai.ac.ae üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Reinforcement learning for solving the vehicle routing problem
M Nazari, A Oroojlooy, LV Snyder, M Takáč
Conference on Neural Information Processing Systems, NeurIPS 2018, 2018
14282018
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P Richtárik, M Takáč
Mathematical Programming 144 (1), 1-38, 2014
8882014
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
7322017
Parallel coordinate descent methods for big data optimization
P Richtárik, M Takáč
Mathematical Programming, Series A, 1-52, 2015
5642015
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 27, 2014
4272014
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
3432015
CoCoA: A general framework for communication-efficient distributed optimization
V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi
Journal of Machine Learning Research 18 (230), 1-49, 2018
3302018
Distributed coordinate descent method for learning with big data
P Richtárik, M Takác
Journal of Machine Learning Research 17, 1-25, 2016
2782016
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
2572018
Distributed learning with compressed gradient differences
K Mishchenko, E Gorbunov, M Takáč, P Richtárik
Optimization Methods and Software, 1-16, 2024
2492024
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
2412017
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
217*2013
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
2112015
A deep q-network for the beer game: Deep reinforcement learning for inventory optimization
A Oroojlooyjadid, MR Nazari, LV Snyder, M Takáč
Manufacturing & Service Operations Management 24 (1), 285-304, 2022
2032022
Applying deep learning to the newsvendor problem
A Oroojlooyjadid, LV Snyder, M Takáč
Iise Transactions 52 (4), 444-463, 2020
1912020
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
1832016
On optimal probabilities in stochastic coordinate descent methods
P Richtárik, M Takáč
Optimization Letters, 2015, 1-11, 2015
1482015
Stochastic recursive gradient algorithm for nonconvex optimization
LM Nguyen, J Liu, K Scheinberg, M Takáč
arXiv preprint arXiv:1705.07261, 2017
1212017
Convolutional neural network approach for robust structural damage detection and localization
NS Gulgec, M Takáč, SN Pakzad
Journal of computing in civil engineering 33 (3), 04019005, 2019
1172019
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
1152016
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