1 code implementation • 5 Mar 2024 • Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horvath, Martin Takac, Eduard Gorbunov
Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive.
no code implementations • 15 Feb 2024 • Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery.
no code implementations • 25 Dec 2023 • Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvath, Martin Takac, Eric Moulines, Maxim Panov
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions.
1 code implementation • 13 Nov 2023 • Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions.
no code implementations • 4 Apr 2023 • Talal Algumaei, Ruben Solozabal, REDA ALAMI, Hakim Hacid, Merouane Debbah, Martin Takac
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.
1 code implementation • 15 Mar 2023 • Nicolas Cuadrado, Roberto Gutierrez, Yongli Zhu, Martin Takac
Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability.
1 code implementation • 9 Jun 2022 • Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac
Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.
no code implementations • 6 Jun 2022 • Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
no code implementations • 10 Mar 2022 • Guangyi Liu, Arash Amini, Martin Takac, Nader Motee
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
no code implementations • 5 Feb 2022 • Yicheng Chen, Rick S. Blum, Martin Takac, Brian M. Sadler
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
1 code implementation • 6 Jul 2021 • Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, Shihao Ji
To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.
Ranked #9 on Text-to-Image Generation on CUB
no code implementations • 14 Jul 2018 • Jie Liu, Yu Rong, Martin Takac, Junzhou Huang
This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.
no code implementations • 16 Dec 2016 • Jie Liu, Martin Takac
We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gradient descent method (SGD).
2 code implementations • 7 Nov 2016 • Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael. I. Jordan, Martin Jaggi
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
no code implementations • 11 Aug 2014 • Jakub Marecek, Peter Richtarik, Martin Takac
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints.