13 citations · 15 across the 5 of their papers we have counts for
6 papers
The Complexity of Finding Local Optima in Contrastive Learning
Jingming Yan, Yiyuan Luo, Vaggos Chatziafratis +3
Contrastive learning is a powerful technique for discovering meaningful data representations by optimizing objectives based on , often given as a…
Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints
Nikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis +2
We consider the problem of computing Nash equilibria in potential games where each player's strategy set is subject to private uncoupled constraints. This scenario is frequently en…
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems
Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki
Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained…
On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford +1
Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and their sensitivity to the initialization process often renders them notoriously hard to train. Recent w…
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas +3
Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, provab…
Reinforcement Learning for Location-Aware Scheduling
Stelios Stavroulakis, Biswa Sengupta
Recent techniques in dynamical scheduling and resource management have found applications in warehouse environments due to their ability to organize and prioritize tasks in a highe…