Martin Takác

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 116

Top Venues

CoRR

ICML

Optim. Methods Softw.

J. Mach. Learn. Res.

Publications

Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows. CoRR (2024)
Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác
Regularization of the policy updates for stabilizing Mean Field Games. CoRR (2023)
Aleksandr Beznosikov, Martin Takác, Alexander V. Gasnikov
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. NeurIPS (2023)
Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takác, Bin Gu
Learning to Control under Time-Varying Environment. CoRR (2022)
Jie Liu, Antonio Bellon, Andrea Simonetto, Martin Takác, Jakub Marecek
Optimal Power Flow Pursuit in the Alternating Current Model. CoRR (2022)
Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler
Distributed Learning With Sparsified Gradient Differences. CoRR (2022)
Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takác, Alexander V. Gasnikov
On Scaled Methods for Saddle Point Problems. CoRR (2022)
Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. CoRR (2022)
Rachael Tappenden, Martin Takác
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points. CoRR (2022)
Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler
Distributed Learning With Sparsified Gradient Differences. IEEE J. Sel. Top. Signal Process. 16 (3) (2022)
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)
Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takác
Learning to generalize Dispatching rules on the Job Shop Scheduling. CoRR (2022)
Artem Agafonov, Brahim Erraji, Martin Takác
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences. CoRR (2022)
Alistair Knott, Mark Sagar, Martin Takác
The ethics of interaction with neurorobotic agents: a case study with BabyX. AI Ethics 2 (1) (2022)
Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower
SP2: A Second Order Stochastic Polyak Method. CoRR (2022)
Guangyi Liu, Arash Amini, Martin Takác, Nader Motee
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input. CoRR (2022)
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)
Zhengqing Gao, Huimin Wu, Martin Takác, Bin Gu
Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. CIKM (2022)
Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác
Algorithm for Constrained Markov Decision Process with Linear Convergence. CoRR (2022)
Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. MICCAI (8) (2022)
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) (2022)
Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác
Stochastic Gradient Methods with Preconditioned Updates. CoRR (2022)
Alistair Knott, Martin Takác
Roles for Event Representations in Sensorimotor Experience, Memory Formation, and Language Processing. Top. Cogn. Sci. 13 (1) (2021)
Lam M. Nguyen, Katya Scheinberg, Martin Takác
Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. 36 (1) (2021)
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) (2021)
Guangyi Liu, Arash Amini, Martin Takác, Nader Motee
Classification-Aware Path Planning of Network of Robots. DARS (2021)
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) (2021)
Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji
Improving Text-to-Image Synthesis Using Contrastive Learning. CoRR (2021)
Mark Sagar, Alecia Moser, Annette M. E. Henderson, Sam Morrison, Nathan Pages, Alireza Nejati, Wan-Ting Yeh, Jonathan Conder, Alistair Knott, Khurram Jawed, Martin Takác
A platform for embodied models of infant cognition, and its use in a model of event perception. ICDL (2021)
Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. AISTATS (2021)
Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji
Improving Text-to-Image Synthesis Using Contrastive Learning. BMVC (2021)
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 (2021)
Aleksandr Beznosikov, Martin Takác
Random-reshuffled SARAH does not need a full gradient computations. CoRR (2021)
Krishnan Kumaran, Dimitri J. Papageorgiou, Martin Takác, Laurens Lueg, Nicolas V. Sahinidis
Active metric learning for supervised classification. Comput. Chem. Eng. 144 (2021)
Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR (2021)
Ruben Solozabal, Josu Ceberio, Martin Takác
Constrained Combinatorial Optimization with Reinforcement Learning. CoRR (2020)
Peter Richtárik, Martin Takác
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. SIAM J. Matrix Anal. Appl. 41 (2) (2020)
Martin Takác, Alistair Knott, Mark Sagar
SOM-Based System for Sequence Chunking and Planning. ICANN (1) (2020)
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)
Guangyi Liu, Arash Amini, Martin Takác, Héctor Muñoz-Avila, Nader Motee
Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure. CoRR (2020)
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 (2020)
Albert S. Berahas, Martin Takác
A robust multi-batch L-BFGS method for machine learning. Optim. Methods Softw. 35 (1) (2020)
Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác
Applying deep learning to the newsvendor problem. IISE Trans. 52 (4) (2020)
Majid Jahani, MohammadReza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takác
Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1. LOD (1) (2020)
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)
Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21 (2020)
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) (2020)
Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takác
Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. CoRR (2020)
Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee
Multi-Agent Image Classification via Reinforcement Learning. IROS (2019)
Konstantin Mishchenko, Eduard A. Gorbunov, Martin Takác, Peter Richtárik
Distributed Learning with Compressed Gradient Differences. CoRR (2019)
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 (2019)
Albert S. Berahas, Majid Jahani, Martin Takác
Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. CoRR (2019)
Mikhail Krechetov, Jakub Marecek, Yury Maximov, Martin Takác
Entropy-Penalized Semidefinite Programming. IJCAI (2019)
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 (2019)
MohammadReza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takác
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework. CoRR (2019)
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác
Distributed Fixed Point Methods with Compressed Iterates. CoRR (2019)
Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee
Multi-Agent Image Classification via Reinforcement Learning. CoRR (2019)
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 (2019)
Jie Liu, Yu Rong, Martin Takác, Junzhou Huang
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches. CoRR (2018)
Xi He, Rachael Tappenden, Martin Takác
Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization. Frontiers Appl. Math. Stat. 4 (2018)
Jakub Marecek, Peter Richtárik, Martin Takác
Matrix Completion Under Interval Uncertainty: Highlights. ECML/PKDD (3) (2018)
Rachael Tappenden, Martin Takác, Peter Richtárik
On the complexity of parallel coordinate descent. Optim. Methods Softw. 33 (2) (2018)
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 (2018)
MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác
Deep Reinforcement Learning for Solving the Vehicle Routing Problem. CoRR (2018)
MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác
Reinforcement Learning for Solving the Vehicle Routing Problem. NeurIPS (2018)
Krishnan Kumaran, Dimitri J. Papageorgiou, Yutong Chang, Minhan Li, Martin Takác
Active Metric Learning for Supervised Classification. CoRR (2018)
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 (2018)
Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
New Convergence Aspects of Stochastic Gradient Algorithms. CoRR (2018)
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)
Lam M. Nguyen, Katya Scheinberg, Martin Takác
Inexact SARAH Algorithm for Stochastic Optimization. CoRR (2018)
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops (2017)
Piotr M. Patrzyk, Martin Takác
Cognitive adaptations to criminal justice lead to "paranoid" norm obedience. Adapt. Behav. 25 (2) (2017)
Chenxin Ma, Martin Takác
Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing. AAAI Workshops (2017)
Albert S. Berahas, Martin Takác
A Robust Multi-Batch L-BFGS Method for Machine Learning. CoRR (2017)
Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR (2017)
Peter Richtárik, Martin Takác
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. CoRR (2017)
Chenxin Ma, Naga Venkata C. Gudapati, Majid Jahani, Rachael Tappenden, Martin Takác
Underestimate Sequences via Quadratic Averaging. CoRR (2017)
Jakub Marecek, Peter Richtárik, Martin Takác
Matrix completion under interval uncertainty. Eur. J. Oper. Res. 256 (1) (2017)
Jakub Marecek, Martin Takác
A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow. Optim. Methods Softw. 32 (4) (2017)
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 (2017)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR (2017)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR (2017)
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) (2017)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML (2017)
Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V. Snyder, Martin Takác
A Deep Q-Network for the Beer Game with Partial Information. CoRR (2017)
Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác
Stock-out Prediction in Multi-echelon Networks. CoRR (2017)
Piotr M. Patrzyk, Martin Takác
Cooperation Via Intimidation: An Emergent System of Mutual Threats can Maintain Social Order. J. Artif. Soc. Soc. Simul. 20 (4) (2017)
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) (2017)
Albert S. Berahas, Jorge Nocedal, Martin Takác
A Multi-Batch L-BFGS Method for Machine Learning. NIPS (2016)
Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi
Primal-Dual Rates and Certificates. CoRR (2016)
Albert S. Berahas, Jorge Nocedal, Martin Takác
A Multi-Batch L-BFGS Method for Machine Learning. CoRR (2016)
Peter Richtárik, Martin Takác
Distributed Coordinate Descent Method for Learning with Big Data. J. Mach. Learn. Res. 17 (2016)
Peter Richtárik, Martin Takác
On optimal probabilities in stochastic coordinate descent methods. Optim. Lett. 10 (6) (2016)
Jie Liu, Martin Takác
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption. CoRR (2016)
Chenxin Ma, Rachael Tappenden, Martin Takác
Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. J. Mach. Learn. Res. 17 (2016)
Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. ICML (2016)
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR (2016)
Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi
Primal-Dual Rates and Certificates. ICML (2016)
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) (2016)
Michael Anton Palkovics, Martin Takác
Exploration of cognition-affect and Type 1-Type 2 dichotomies in a computational model of decision making. Cogn. Syst. Res. 40 (2016)

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Publication Activity (10 Years)

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Martin Takác