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ORCIDMartin Takác 0001
Person information
- affiliation: Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
- affiliation (former): Lehigh University, Bethlehem, PA, USA
Other persons with the same name
- Martin Takác 0002
![[0000-0002-3956-962X]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-3956-962X")
— Comenius University, Bratislava, Slovak Republic (and 1 more)
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Journal Articles
- 2024
[j33]Vitali Pirau, Aleksandr Beznosikov, Martin Takác, Vladislav Matyukhin, Alexander V. Gasnikov:
Preconditioning meets biased compression for efficient distributed optimization. Comput. Manag. Sci. 21(1): 14 (2024)- [j32]Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác:
Stochastic Gradient Methods with Preconditioned Updates. J. Optim. Theory Appl. 201(2): 471-489 (2024) - [j31]Aleksandr Beznosikov, Martin Takác:
Random-reshuffled SARAH does not need full gradient computations. Optim. Lett. 18(3): 727-749 (2024) - 2023
- [j30]Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác:
Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands. IEEE Access 11: 87958-87969 (2023) - [j29]Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác:
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j28]Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takác, Alexander V. Gasnikov:
Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. EURO J. Comput. Optim. 10: 100041 (2022) - [j27]Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler:
Distributed Learning With Sparsified Gradient Differences. IEEE J. Sel. Top. Signal Process. 16(3): 585-600 (2022) - [j26]Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V. Snyder, Martin Takác:
A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization. Manuf. Serv. Oper. Manag. 24(1): 285-304 (2022) - [j25]Albert S. Berahas, Majid Jahani, Peter Richtárik, Martin Takác:
Quasi-Newton methods for machine learning: forget the past, just sample. Optim. Methods Softw. 37(5): 1668-1704 (2022) - 2021
- [j24]Krishnan Kumaran, Dimitri J. Papageorgiou, Martin Takác, Laurens Lueg, Nicolas V. Sahinidis:
Active metric learning for supervised classification. Comput. Chem. Eng. 144: 107132 (2021) - [j23]Majid Jahani, Naga Venkata C. Gudapati, Chenxin Ma, Rachael Tappenden, Martin Takác:
Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences. Comput. Optim. Appl. 79(2): 369-404 (2021) - [j22]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. 36(1): 20-44 (2021) - [j21]Lam M. Nguyen, Katya Scheinberg, Martin Takác:
Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. 36(1): 237-258 (2021) - 2020
- [j20]Nur Sila Gulgec, Martin Takác, Shamim N. Pakzad:
Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment. Comput. Aided Civ. Infrastructure Eng. 35(12): 1349-1364 (2020) - [j19]Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác:
Applying deep learning to the newsvendor problem. IISE Trans. 52(4): 444-463 (2020) - [j18]Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro:
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21: 120:1-120:51 (2020) - [j17]Albert S. Berahas, Martin Takác:
A robust multi-batch L-BFGS method for machine learning. Optim. Methods Softw. 35(1): 191-219 (2020) - [j16]Peter Richtárik, Martin Takác:
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. SIAM J. Matrix Anal. Appl. 41(2): 487-524 (2020) - 2019
- [j15]Nur Sila Gulgec, Martin Takác, Shamim N. Pakzad:
Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization. J. Comput. Civ. Eng. 33(3) (2019) - [j14]Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk:
New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. 20: 176:1-176:49 (2019) - 2018
- [j13]Xi He, Rachael Tappenden, Martin Takác:
Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization. Frontiers Appl. Math. Stat. 4: 33 (2018) - [j12]Rachael Tappenden, Martin Takác, Peter Richtárik:
On the complexity of parallel coordinate descent. Optim. Methods Softw. 33(2): 372-395 (2018) - 2017
- [j11]Jakub Marecek, Peter Richtárik, Martin Takác:
Matrix completion under interval uncertainty. Eur. J. Oper. Res. 256(1): 35-43 (2017) - [j10]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. 18: 230:1-230:49 (2017) - [j9]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): 813-848 (2017) - [j8]Jakub Marecek, Martin Takác:
A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow. Optim. Methods Softw. 32(4): 849-871 (2017) - [j7]Jie Liu, Alan C. Liddell Jr., Jakub Marecek, Martin Takác:
Hybrid Methods in Solving Alternating-Current Optimal Power Flows. IEEE Trans. Smart Grid 8(6): 2988-2998 (2017) - 2016
- [j6]Peter Richtárik, Martin Takác:
Distributed Coordinate Descent Method for Learning with Big Data. J. Mach. Learn. Res. 17: 75:1-75:25 (2016) - [j5]Chenxin Ma, Rachael Tappenden, Martin Takác:
Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. J. Mach. Learn. Res. 17: 230:1-230:24 (2016) - [j4]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): 242-255 (2016) - [j3]Peter Richtárik, Martin Takác:
Parallel coordinate descent methods for big data optimization. Math. Program. 156(1-2): 433-484 (2016) - [j2]Peter Richtárik, Martin Takác:
On optimal probabilities in stochastic coordinate descent methods. Optim. Lett. 10(6): 1233-1243 (2016) - 2014
- [j1]Peter Richtárik, Martin Takác:
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Program. 144(1-2): 1-38 (2014)
Conference and Workshop Papers
- 2024
- [c37]Kun Song, Ruben Solozabal, Hao Li, Martin Takác, Lu Ren, Fakhri Karray:
Robustly Train Normalizing Flows via KL Divergence Regularization. AAAI 2024: 15047-15055 - [c36]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. AISTATS 2024: 4879-4887 - 2023
- [c35]Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác:
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. ACML 2023: 502-517 - [c34]Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác:
Algorithm for Constrained Markov Decision Process with Linear Convergence. AISTATS 2023: 11506-11533 - [c33]Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower:
SP2 : A Second Order Stochastic Polyak Method. ICLR 2023 - [c32]Nicolas M. Cuadrado, Roberto Alejandro Gutiérrez Guillén, Martin Takác:
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. Tiny Papers @ ICLR 2023 - [c31]Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal Ochoa de Retana, Martin Takác:
On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling. IJCAI 2023: 5350-5358 - [c30]Aleksandr Beznosikov, Martin Takác, Alexander V. Gasnikov:
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. NeurIPS 2023 - [c29]Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov:
Byzantine-Tolerant Methods for Distributed Variational Inequalities. NeurIPS 2023 - [c28]Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác:
Regularization of the Policy Updates for Stabilizing Mean Field Games. PAKDD (2) 2023: 361-372 - 2022
- [c27]Zhengqing Gao, Huimin Wu, Martin Takác, Bin Gu:
Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. CIKM 2022: 3072-3081 - [c26]Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác:
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR 2022 - [c25]Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu:
The power of first-order smooth optimization for black-box non-smooth problems. ICML 2022: 7241-7265 - [c24]Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar:
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. MICCAI (8) 2022: 673-683 - [c23]Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác:
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. NeurIPS 2022 - 2021
- [c22]Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác:
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. AISTATS 2021: 487-495 - [c21]Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji:
Improving Text-to-Image Synthesis Using Contrastive Learning. BMVC 2021: 154 - [c20]Guangyi Liu, Arash Amini, Martin Takác, Nader Motee:
Classification-Aware Path Planning of Network of Robots. DARS 2021: 294-305 - 2020
- [c19]Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác:
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. AISTATS 2020: 2634-2644 - [c18]Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takác:
Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. ICMLA 2020: 130-135 - [c17]Majid Jahani, MohammadReza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takác:
Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1. LOD (1) 2020: 41-54 - 2019
- [c16]Mikhail Krechetov, Jakub Marecek, Yury Maximov, Martin Takác:
Entropy-Penalized Semidefinite Programming. IJCAI 2019: 1123-1129 - [c15]Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee:
Multi-Agent Image Classification via Reinforcement Learning. IROS 2019: 5020-5027 - 2018
- [c14]Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác:
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. ICML 2018: 3747-3755 - [c13]MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác:
Reinforcement Learning for Solving the Vehicle Routing Problem. NeurIPS 2018: 9861-9871 - [c12]Jakub Marecek, Peter Richtárik, Martin Takác:
Matrix Completion Under Interval Uncertainty: Highlights. ECML/PKDD (3) 2018: 621-625 - 2017
- [c11]Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác:
Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops 2017 - [c10]Chenxin Ma, Martin Takác:
Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing. AAAI Workshops 2017 - [c9]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML 2017: 2613-2621 - 2016
- [c8]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. ICML 2016: 783-792 - [c7]Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq:
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. ICML 2016: 1823-1832 - [c6]Albert S. Berahas, Jorge Nocedal, Martin Takác:
A Multi-Batch L-BFGS Method for Machine Learning. NIPS 2016: 1055-1063 - 2015
- [c5]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML 2015: 1973-1982 - 2014
- [c4]Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác:
Fast distributed coordinate descent for non-strongly convex losses. MLSP 2014: 1-6 - [c3]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS 2014: 3068-3076 - 2013
- [c2]Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro:
Mini-Batch Primal and Dual Methods for SVMs. ICML (3) 2013: 1022-1030 - 2011
- [c1]Peter Richtárik, Martin Takác:
Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design. OR 2011: 27-32
Informal and Other Publications
- 2024
- [i86]Nazarii Tupitsa, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Federated Learning Can Find Friends That Are Beneficial. CoRR abs/2402.05050 (2024) - [i85]Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang, Alexander V. Gasnikov, Martin Takác, Dmitry Kamzolov:
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size. CoRR abs/2402.05264 (2024) - [i84]Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác:
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows. CoRR abs/2402.09765 (2024) - [i83]Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. CoRR abs/2403.02648 (2024) - [i82]Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takác:
Generalized Policy Learning for Smart Grids: FL TRPO Approach. CoRR abs/2403.18439 (2024) - [i81]Nicolas Mauricio Cuadrado, Roberto Alejandro Gutiérrez Guillén, Martin Takác:
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. CoRR abs/2403.18444 (2024) - [i80]Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác:
Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR abs/2404.07525 (2024) - [i79]Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takác:
Self-Guiding Exploration for Combinatorial Problems. CoRR abs/2405.17950 (2024) - [i78]Saveliy Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Aleksandr Beznosikov, Martin Takác, Yaroslav Kholodov, Alexander V. Gasnikov:
Local Methods with Adaptivity via Scaling. CoRR abs/2406.00846 (2024) - 2023
- [i77]Asma Ahmed Hashmi, Artem Agafonov, Aigerim Zhumabayeva, Mohammad Yaqub, Martin Takác:
In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise. CoRR abs/2301.00524 (2023) - [i76]Nicolas M. Cuadrado, Roberto A. Gutiérrez, Yongli Zhu, Martin Takác:
MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids. CoRR abs/2303.08447 (2023) - [i75]Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác:
Regularization of the policy updates for stabilizing Mean Field Games. CoRR abs/2304.01547 (2023) - [i74]Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Martin Takác:
Stochastic Gradient Descent with Preconditioned Polyak Step-size. CoRR abs/2310.02093 (2023) - [i73]Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov:
Byzantine-Tolerant Methods for Distributed Variational Inequalities. CoRR abs/2311.04611 (2023) - [i72]Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác:
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. CoRR abs/2311.07708 (2023) - [i71]Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov:
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR abs/2312.11230 (2023) - [i70]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. CoRR abs/2312.15799 (2023) - [i69]Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Robert M. Gower, Martin Takác:
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. CoRR abs/2312.17369 (2023) - 2022
- [i68]Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler:
Distributed Learning With Sparsified Gradient Differences. CoRR abs/2202.02491 (2022) - [i67]Guangyi Liu, Arash Amini, Martin Takác, Nader Motee:
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input. CoRR abs/2203.05403 (2022) - [i66]Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác:
Stochastic Gradient Methods with Preconditioned Updates. CoRR abs/2206.00285 (2022) - [i65]Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác:
Algorithm for Constrained Markov Decision Process with Linear Convergence. CoRR abs/2206.01666 (2022) - [i64]Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takác, Bin Gu:
Learning to Control under Time-Varying Environment. CoRR abs/2206.02507 (2022) - [i63]Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takác:
Learning to generalize Dispatching rules on the Job Shop Scheduling. CoRR abs/2206.04423 (2022) - [i62]Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takác, Alexander V. Gasnikov:
On Scaled Methods for Saddle Point Problems. CoRR abs/2206.08303 (2022) - [i61]Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower:
SP2: A Second Order Stochastic Polyak Method. CoRR abs/2207.08171 (2022) - [i60]Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar:
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. CoRR abs/2207.10804 (2022) - [i59]Artem Agafonov, Brahim Erraji, Martin Takác:
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences. CoRR abs/2210.09626 (2022) - [i58]Rachael Tappenden, Martin Takác:
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points. CoRR abs/2211.00866 (2022) - [i57]Jie Liu, Antonio Bellon, Andrea Simonetto, Martin Takác, Jakub Marecek:
Optimal Power Flow Pursuit in the Alternating Current Model. CoRR abs/2211.02939 (2022) - [i56]Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác:
Partial Disentanglement with Partially-Federated GANs (PaDPaF). CoRR abs/2212.03836 (2022) - 2021
- [i55]Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác:
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR abs/2102.09700 (2021) - [i54]Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji:
Improving Text-to-Image Synthesis Using Contrastive Learning. CoRR abs/2107.02423 (2021) - [i53]Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác:
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR abs/2109.05198 (2021) - [i52]Aleksandr Beznosikov, Martin Takác:
Random-reshuffled SARAH does not need a full gradient computations. CoRR abs/2111.13322 (2021) - 2020
- [i51]Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takác:
Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. CoRR abs/2006.01892 (2020) - [i50]Ruben Solozabal, Josu Ceberio, Martin Takác:
Constrained Combinatorial Optimization with Reinforcement Learning. CoRR abs/2006.11984 (2020) - [i49]Soheil Sadeghi Eshkevari, Martin Takác, Shamim N. Pakzad, Majid Jahani:
DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction. CoRR abs/2007.01814 (2020) - [i48]Guangyi Liu, Arash Amini, Martin Takác, Héctor Muñoz-Avila, Nader Motee:
Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure. CoRR abs/2012.10480 (2020) - 2019
- [i47]Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik:
Distributed Learning with Compressed Gradient Differences. CoRR abs/1901.09269 (2019) - [i46]Albert S. Berahas, Majid Jahani, Martin Takác:
Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. CoRR abs/1901.09997 (2019) - [i45]Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee:
Multi-Agent Image Classification via Reinforcement Learning. CoRR abs/1905.04835 (2019) - [i44]MohammadReza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takác:
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework. CoRR abs/1905.13562 (2019) - [i43]Hossein K. Mousavi, Guangyi Liu, Weihang Yuan, Martin Takác, Héctor Muñoz-Avila, Nader Motee:
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning. CoRR abs/1909.09705 (2019) - [i42]Nur Sila Gulgec, Zheng Shi, Neil Deshmukh, Shamim N. Pakzad, Martin Takác:
FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods. CoRR abs/1910.12680 (2019) - [i41]Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác:
Distributed Fixed Point Methods with Compressed Iterates. CoRR abs/1912.09925 (2019) - 2018
- [i40]Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác:
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. CoRR abs/1802.03801 (2018) - [i39]MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác:
Deep Reinforcement Learning for Solving the Vehicle Routing Problem. CoRR abs/1802.04240 (2018) - [i38]Krishnan Kumaran, Dimitri J. Papageorgiou, Yutong Chang, Minhan Li, Martin Takác:
Active Metric Learning for Supervised Classification. CoRR abs/1803.10647 (2018) - [i37]Jie Liu, Yu Rong, Martin Takác, Junzhou Huang:
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches. CoRR abs/1807.05328 (2018) - [i36]Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác:
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. CoRR abs/1810.11507 (2018) - [i35]Lam M. Nguyen, Katya Scheinberg, Martin Takác:
Inexact SARAH Algorithm for Stochastic Optimization. CoRR abs/1811.10105 (2018) - [i34]Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk:
New Convergence Aspects of Stochastic Gradient Algorithms. CoRR abs/1811.12403 (2018) - 2017
- [i33]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR abs/1703.00102 (2017) - [i32]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR abs/1705.07261 (2017) - [i31]Peter Richtárik, Martin Takác:
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. CoRR abs/1706.01108 (2017) - [i30]Albert S. Berahas, Martin Takác:
A Robust Multi-Batch L-BFGS Method for Machine Learning. CoRR abs/1707.08552 (2017) - [i29]Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V. Snyder, Martin Takác:
A Deep Q-Network for the Beer Game with Partial Information. CoRR abs/1708.05924 (2017) - [i28]Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác:
Stock-out Prediction in Multi-echelon Networks. CoRR abs/1709.06922 (2017) - [i27]Chenxin Ma, Naga Venkata C. Gudapati, Majid Jahani, Rachael Tappenden, Martin Takác:
Underestimate Sequences via Quadratic Averaging. CoRR abs/1710.03695 (2017) - [i26]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR abs/1711.05305 (2017) - 2016
- [i25]Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. CoRR abs/1602.05205 (2016) - [i24]Chenxin Ma, Martin Takác:
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing. CoRR abs/1603.05191 (2016) - [i23]Albert S. Berahas, Jorge Nocedal, Martin Takác:
A Multi-Batch L-BFGS Method for Machine Learning. CoRR abs/1605.06049 (2016) - [i22]Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác:
Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR abs/1606.00511 (2016) - [i21]Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác:
Applying Deep Learning to the Newsvendor Problem. CoRR abs/1607.02177 (2016) - [i20]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR abs/1611.02189 (2016) - [i19]Jie Liu, Martin Takác:
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption. CoRR abs/1612.05356 (2016) - 2015
- [i18]Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq:
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. CoRR abs/1502.02268 (2015) - [i17]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR abs/1502.03508 (2015) - [i16]Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác:
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR abs/1504.04407 (2015) - [i15]Chenxin Ma, Rachael Tappenden, Martin Takác:
Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption. CoRR abs/1506.02530 (2015) - [i14]Martin Takác, Peter Richtárik, Nathan Srebro:
Distributed Mini-Batch SDCA. CoRR abs/1507.08322 (2015) - [i13]Xi He, Martin Takác:
Dual Free SDCA for Empirical Risk Minimization with Adaptive Probabilities. CoRR abs/1510.06684 (2015) - [i12]Chenxin Ma, Martin Takác:
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization? CoRR abs/1510.06688 (2015) - [i11]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed Optimization with Arbitrary Local Solvers. CoRR abs/1512.04039 (2015) - 2014
- [i10]Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác:
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses. CoRR abs/1405.5300 (2014) - [i9]Martin Takác, Jakub Marecek, Peter Richtárik:
Inequality-Constrained Matrix Completion: Adding the Obvious Helps! CoRR abs/1408.2467 (2014) - [i8]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR abs/1409.1458 (2014) - [i7]Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác:
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR abs/1410.4744 (2014) - 2013
- [i6]Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nathan Srebro:
Mini-Batch Primal and Dual Methods for SVMs. CoRR abs/1303.2314 (2013) - [i5]Peter Richtárik, Martin Takác:
Distributed Coordinate Descent Method for Learning with Big Data. CoRR abs/1310.2059 (2013) - [i4]Peter Richtárik, Martin Takác:
On Optimal Probabilities in Stochastic Coordinate Descent Methods. CoRR abs/1310.3438 (2013) - [i3]Martin Takác, Selin Damla Ahipasaoglu, Ngai-Man Cheung, Peter Richtárik:
TOP-SPIN: TOPic discovery via Sparse Principal component INterference. CoRR abs/1311.1406 (2013) - 2012
- [i2]Peter Richtárik, Martin Takác:
Parallel Coordinate Descent Methods for Big Data Optimization. CoRR abs/1212.0873 (2012) - [i1]Peter Richtárik, Martin Takác, Selin Damla Ahipasaoglu:
Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes. CoRR abs/1212.4137 (2012)
Coauthor Index
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