260 citations · 1.8k across the 63 of their papers we have counts for
73 papers
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
Aya El Mir, Martin Takáč, Salem Lahlou
Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints remains poorly understood.…
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takac +2
Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to captur…
Self-Guiding Exploration for Combinatorial Problems
Zangir Iklassov, Yali Du, Farkhad Akimov +1
Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize promp…
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Takáč
The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part…
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal +1
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 deli…
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…
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points
Rachael Tappenden, Martin Takáč
This work shows that applying Gradient Descent (GD) with a fixed step size to minimize a (possibly nonconvex) quadratic function is equivalent to running the Power Method (PM) on t…
Optimal Power Flow Pursuit in the Alternating Current Model
Jie Liu, Antonio Bellon, Andrea Simonetto +2
Transmission-constrained problems in power systems can be cast as polynomial optimization problems whose coefficients vary over time. We consider the complications therein and sugg…
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences
Artem Agafonov, Brahim Erraji, Martin Takáč
In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order Framework via Compression and Sketching), the second-order framework FLECS was proposed for the…
Learning to Control under Time-Varying Environment
Yuzhen Han, Ruben Solozabal, Jing Dong +3
This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, des…
On Scaled Methods for Saddle Point Problems
Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev +2
Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning pr…
Learning to generalize Dispatching rules on the Job Shop Scheduling
Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal +1
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not…
Algorithm for Constrained Markov Decision Process with Linear Convergence
Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina +3
The problem of constrained Markov decision process is considered. An agent aims to maximize the expected accumulated discounted reward subject to multiple constraints on its costs…
Stochastic Gradient Methods with Preconditioned Updates
Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori +3
This work considers the non-convex finite sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly…
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
Guangyi Liu, Arash Amini, Martin Takac +1
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…
Distributed Learning With Sparsified Gradient Differences
Yicheng Chen, Rick S. Blum, Martin Takac +1
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communica…
The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii +6
Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper,…
Random-reshuffled SARAH does not need a full gradient computations
Aleksandr Beznosikov, Martin Takáč
The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a variance reduced variant of the Stochastic Gradient Descent (SGD) algorithm that needs a gradient of the objectiv…
Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes
Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov +5
This paper considers the problem of decentralized, personalized federated learning. For centralized personalized federated learning, a penalty that measures the deviation from the…