260 citations · 1.8k across the 63 of their papers we have counts for
6 papers · 1 filter
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal +1
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 invo…
Byzantine-Tolerant Methods for Distributed Variational Inequalities
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu +4
Robustness to Byzantine attacks is a necessity for various distributed training scenarios. When the training reduces to the process of solving a minimization problem, Byzantine rob…
Stochastic Gradient Descent with Preconditioned Polyak Step-size
Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov +1
Stochastic Gradient Descent (SGD) is one of the many iterative optimization methods that are widely used in solving machine learning problems. These methods display valuable proper…
Regularization of the policy updates for stabilizing Mean Field Games
Talal Algumaei, Ruben Solozabal, Reda Alami +3
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 retu…
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition
Dávid Šuba, Marek Šuppa, Jozef Kubík +2
Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manua…
MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids
Nicolas Cuadrado, Roberto Gutierrez, Yongli Zhu +1
Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and poll…