4 papers
eCP: Equivariant Conformal Prediction with pre-trained models
Nikolaos Bousias, Lars Lindemann, George Pappas
Conformal prediction, a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchange…
On transferring safety certificates across dynamical systems
Nikolaos Bousias, Charalampia Stamouli, Anastasios Tsiamis +1
Control barrier functions (CBFs) provide a powerful tool for enforcing safety constraints in control systems, but their direct application to complex, high-dimensional dynamics is…
Deep Equivariant Multi-Agent Control Barrier Functions
Nikolaos Bousias, Lars Lindemann, George Pappas
With multi-agent systems increasingly deployed autonomously at scale in complex environments, ensuring safety of the data-driven policies is critical. Control Barrier Functions hav…
Symmetries-enhanced Multi-Agent Reinforcement Learning
Nikolaos Bousias, Stefanos Pertigkiozoglou, Kostas Daniilidis +1
Multi-agent reinforcement learning has emerged as a powerful framework for enabling agents to learn complex, coordinated behaviors but faces persistent challenges regarding its gen…