260 citations · 1.8k across the 63 of their papers we have counts for
34 papers · 1 filter
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.…
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…
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…
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…
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…
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…
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…
Improving Text-to-Image Synthesis Using Contrastive Learning
Hui Ye, Xiulong Yang, Martin Takac +2
The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same imag…
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
Mohammadreza Nazari, Majid Jahani, Lawrence V. Snyder +1
Although reinforcement learning (RL) can provide reliable solutions in many settings, practitioners are often wary of the discrepancies between the RL solution and their status quo…
Multi-Agent Image Classification via Reinforcement Learning
Hossein K. Mousavi, Mohammadreza Nazari, Martin Takáč +1
We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to clas…
Distributed Learning with Compressed Gradient Differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč +1
Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods bas…
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
Majid Jahani, Xi He, Chenxin Ma +4
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empiric…
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches
Jie Liu, Yu Rong, Martin Takac +1
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.…
Active Metric Learning for Supervised Classification
Krishnan Kumaran, Dimitri Papageorgiou, Yutong Chang +2
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find…
Stock-out Prediction in Multi-echelon Networks
Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč
In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disrup…
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
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 gra…
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith, Simone Forte, Chenxin Ma +3
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed…
Applying Deep Learning to the Newsvendor Problem
Afshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč
The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is…
Distributed Hessian-Free Optimization for Deep Neural Network
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy +1
Training deep neural network is a high dimensional and a highly non-convex optimization problem. Stochastic gradient descent (SGD) algorithm and it's variations are the current sta…