Published in The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024
We propose a method for optimization with unknown and time-varying optimization objective and constraints, based on Bayesian optimization.
Recommended citation: YJialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
Download Paper | Download Slides
Published in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024, 2024
A book chapter, focused on advanced control methods, suitable for industrial applications in robotics and precision motion systems.
Recommended citation: Alisa Rupenyan, Efe C. Balta (2024). "Robotics and Manufacturing Application." The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024.
Download Paper
Published in Engineering Applications of Artificial Intelligence, volume 143, 1 March 2025, 109894, 2025
We propose a method for safe and fast data-driven optimization by formulating a series of optimization problems instead of a grid search.
Recommended citation: YJialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
Download Paper
Published in IEEE Robotics and Automation Letters, presented on IEEE ICRA 2023, 2023
RAGoOSe is a novel data-driven approach that combines safe learning with risk-averse Bayesian optimization to safely tune controllers in high-precision systems with variable noise, demonstrating improved performance over traditional methods in both synthetic and real semiconductor manufacturing applications.
Recommended citation: Christopher König, Miks Ozols, Anastasia Makarova, Efe C Balta, Andreas Krause, Alisa Rupenyan (2023). "Safe risk-averse bayesian optimization for controller tuning." IEEE Robotics and Automation Letters. 8(8208 - 8215).
Download Paper | Download Slides