1 citations · 2 across the 6 of their papers we have counts for
6 papers
Automatic Prompt Optimization for Dataset-Level Feature Discovery
Adrian Cosma, Oleg Szehr, David Kletz +2
Feature extraction from unstructured text is a critical step in many downstream classification pipelines, yet current approaches largely rely on hand-crafted prompts or fixed featu…
Spectral Criteria for Uniqueness Pairs of Unitary Transforms
Oleg Szehr
The identification of sampling sets that enable unique signal recovery is fundamental to many applications in signal processing and remains a central problem in mathematical analys…
Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning
Ardian Selmonaj, Miroslav Strupl, Oleg Szehr +1
To reliably deploy Multi-Agent Reinforcement Learning (MARL) systems, it is crucial to understand individual agent behaviors. While prior work typically evaluates overall team perf…
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3
This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to iden…
On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
Miroslav Štrupl, Oleg Szehr, Francesco Faccio +3
This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Tran…
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3
The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the hig…