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Takáč, Martin

  1. Reinforcement Learning for Solving the Vehicle Routing Problem
    2018/02/12 by Nazari, Mohammadreza, Oroojlooy, Afshin, Snyder, Lawrence V. +1 · 23 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  2. Distributed Learning with Compressed Gradient Differences
    2019/01/26 by Konstantin Mishchenko, Mishchenko, Konstantin, Eduard Gorbunov +5 · 23 citations
    Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  3. Iteration Complexity of Randomized Block-Coordinate Descent Methods for\n Minimizing a Composite Function
    2011/07/14 by Peter Richtárik, Martin Takáč, Richtárik, Peter +1 · 10 citations
    Computer Science · Engineering · #Complexity and Algorithms in Graphs #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #Machine Learning (stat.ML) #Machine Learning and Algorithms #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  4. Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample
    2019/01/28 by Berahas, Albert S., Jahani, Majid, Richtárik, Peter +1 · 5 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  5. Applying Deep Learning to the Newsvendor Problem
    2016/07/07 by Oroojlooyjadid, Afshin, Snyder, Lawrence, Takáč, Martin · 4 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  6. From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
    2024/02/16 by Nikita Kotelevskii, Kotelevskii, Nikita, Kondratyev, Vladimir +3 · 10 citations
    Computer Science · #Advanced Database Systems and Queries #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  7. SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
    2015/02/08 by Qu, Zheng, Richtárik, Peter, Takáč, Martin +1 · 4 citations
    Computer Science · Engineering · #3D Shape Modeling and Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  8. A Multi-Batch L-BFGS Method for Machine Learning
    2016/05/19 by Albert S. Berahas, Jorge Nocedal, Berahas, Albert S. +3 · 3 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  9. Methods for Convex (L0,L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity
    2024/09/23 by Gorbunov, Eduard, Tupitsa, Nazarii, Choudhury, Sayantan +4 · 9 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  10. Parallel Coordinate Descent Methods for Big Data Optimization
    2012/12/04 by Peter Richtárik, Richtárik, Peter, Martin Takáč +1 · 4 citations
    Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Complexity and Algorithms in Graphs #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  11. Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness
    2023/09/04 by Artem Agafonov, Agafonov, Artem, Dmitry Kamzolov +11 · 5 citations
    Mathematics · Computer Science · Engineering · #Tensor decomposition and applications #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques
  12. Inexact Tensor Methods and Their Application to Stochastic Convex Optimization
    2020/12/31 by Agafonov, Artem, Kamzolov, Dmitry, Dvurechensky, Pavel +2 · 4 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  13. Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of O(k-2) for Convex Functions
    2023/02/10 by Kamzolov, Dmitry, Ziu, Klea, Agafonov, Artem +1 · 4 citations
    #FOS: Mathematics #Optimization and Control (math.OC)
  14. Mini-Batch Primal and Dual Methods for SVMs
    2013/03/10 by Martin Takáč, Takáč, Martin, Avleen S. Bijral +5 · 3 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Neural Networks and Applications #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  15. Distributed Fixed Point Methods with Compressed Iterates
    2019/12/20 by Chraibi, Sélim, Khaled, Ahmed, Kovalev, Dmitry +3 · 2 citations
    #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Parallel #and Cluster Computing (cs.DC)
  16. Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
    2021/09/11 by Jahani, Majid, Rusakov, Sergey, Shi, Zheng +3 · 2 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  17. Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
    2024/06/06 by Savelii Chezhegov, Yaroslav Klyukin, Chezhegov, Savelii +13 · 3 citations
    Computer Science · Neuroscience · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Neural Networks and Applications #Neural dynamics and brain function #Optimization and Control (math.OC)
  18. Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes
    2012/12/17 by Richtárik, Peter, Jahani, Majid, Ahipaşaoğlu, Selin Damla +1 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  19. Communication-Efficient Distributed Dual Coordinate Ascent
    2014/09/04 by Jaggi, Martin, Smith, Virginia, Takáč, Martin +4 · 1 citation
    #68W15 #90C25 #C.1.4 #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  20. Stochastic Reformulations of Linear Systems: Algorithms and Convergence\n Theory
    2017/06/04 by Peter Richtárik, Richtárik, Peter, Martin Takáč +1 · 1 citation
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matrix Theory and Algorithms #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  21. Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
    2017/05/20 by Nguyen, Lam M., Liu, Jie, Scheinberg, Katya +1 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  22. AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
    2021/02/19 by Shi, Zheng, Sadiev, Abdurakhmon, Loizou, Nicolas +2 · 2 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  23. A Robust Multi-Batch L-BFGS Method for Machine Learning
    2017/07/26 by Berahas, Albert S., Takáč, Martin · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  24. Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under (L0,L1)-Smoothness
    2024/12/22 by Aleksandr Lobanov, Alexander Gasnikov, Lobanov, Aleksandr +5 · 4 citations
    Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #Iterative Methods for Nonlinear Equations #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques
  25. New Convergence Aspects of Stochastic Gradient Algorithms
    2018/11/10 by Nguyen, Lam M., Nguyen, Phuong Ha, Richtárik, Peter +3 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  26. Multi-Agent Image Classification via Reinforcement Learning
    2019/05/13 by Mousavi, Hossein K., Nazari, Mohammadreza, Takáč, Martin +1 · 1 citation
    #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multiagent Systems (cs.MA) #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
  27. Stochastic Gradient Descent with Preconditioned Polyak Step-size
    2023/10/03 by Farshed Abdukhakimov, Chulu Xiang, Abdukhakimov, Farshed +5 · 2 citations
    Computer Science · #Stochastic Gradient Optimization Techniques #Machine Learning and Algorithms #Machine Learning and ELM
  28. Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
    2024/05/25 by Artem Agafonov, Agafonov, Artem, Petr Ostroukhov +13 · 2 citations
    Mathematics · Computer Science · Engineering · #Advanced Optimization Algorithms Research #Optimization and Variational Analysis #Advanced Control Systems Optimization
  29. SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms
    2023/12/28 by Farshed Abdukhakimov, Chulu Xiang, Abdukhakimov, Farshed +7 · 2 citations
    Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning and ELM #Neural Networks and Applications #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  30. PaDPaF: Partial Disentanglement with Partially-Federated GANs
    2022/12/07 by Abdulla Jasem Almansoori, Almansoori, Abdulla Jasem, Samuel Horváth +3 · 1 citation
    Computer Science · #Advanced Steganography and Watermarking Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data
  31. Inexact SARAH Algorithm for Stochastic Optimization
    2018/11/25 by Nguyen, Lam M., Scheinberg, Katya, Takáč, Martin · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  32. Distributed Coordinate Descent Method for Learning with Big Data
    2013/10/08 by Richtárik, Peter, Takáč, Martin · 1 citation
    #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Parallel #and Cluster Computing (cs.DC)
  33. SP2: A Second Order Stochastic Polyak Method
    2022/07/17 by Li, Shuang, Swartworth, William J., Takáč, Martin +2 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  34. MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
    2024/06/13 by Fares, Samar, Ziu, Klea, Aremu, Toluwani +5 · 1 citation
    #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  35. FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
    2024/11/12 by Zmushko, Philip, Beznosikov, Aleksandr, Takáč, Martin +1 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  36. Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
    2023/12/18 by Kotelevskii, Nikita, Horváth, Samuel, Nandakumar, Karthik +2 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  37. FLECS: A Federated Learning Second-Order Framework via Compression and Sketching
    2022/06/04 by Agafonov, Artem, Kamzolov, Dmitry, Tappenden, Rachael +2 · 1 citation
    #FOS: Mathematics #Optimization and Control (math.OC)
  38. Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
    2024/12/03 by Demidovich, Yury, Ostroukhov, Petr, Malinovsky, Grigory +4 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  39. FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences
    2022/10/18 by Agafonov, Artem, Erraji, Brahim, Takáč, Martin · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)