Licio Romao

CoRR, April, 2026

2025

Data-Driven Memory-Dependent Abstractions of Dynamical Systems via a Cantor-Kantorovich Metric.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., December, 2025

Exact Recourse Functions for Aggregations of EVs Operating in Imbalance Markets.

[BibT_eX]

[DOI]

Karan Mukhi

Alessandro Abate

CoRR, December, 2025

Risk-Averse Certification of Bayesian Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Dependable Software Engineering. Theories, Tools, and Applications, 2025

Bridging conformal prediction and scenario optimization.

[BibT_eX]

[DOI]

Niall O'Sullivan

Frederik Baymler Mathiesen

Proceedings of the 64th IEEE Conference on Decision and Control, 2025

2024

DynAbs: Abstraction-Based Controller Synthesis for Stochastic Linear Dynamical Systems.

[BibT_eX]

[DOI]

Dataset, August, 2024

Enhancing Data-Driven Stochastic Control via Bundled Interval MDP.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2024

A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution.

[BibT_eX]

[DOI]

CoRR, 2024

Data-driven Interval MDP for Robust Control Synthesis.

[BibT_eX]

[DOI]

CoRR, 2024

Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers.

[BibT_eX]

[DOI]

Mykel J. Kochenderfer

CoRR, 2024

Bounded robustness in reinforcement learning via lexicographic objectives.

[BibT_eX]

[DOI]

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions.

[BibT_eX]

[DOI]

Proceedings of the European Control Conference, 2024

2023

Probabilistic feasibility guarantees for convex scenario programs with an arbitrary number of discarded constraints.

[BibT_eX]

[DOI]

Autom., March, 2023

On the Exact Feasibility of Convex Scenario Programs With Discarded Constraints.

[BibT_eX]

[DOI]

Maico Hendrikus Wilhelmus Engelaar

IEEE Trans. Autom. Control., 2023

Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2023

Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions.

[BibT_eX]

[DOI]

Proceedings of the Proceedings Fifth International Workshop on Formal Methods for Autonomous Systems, 2023

Model Reduction of Linear Stochastic Systems with Preservation of sc-LTL Specifications.

[BibT_eX]

[DOI]

CoRR, 2023

Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version].

[BibT_eX]

[DOI]

CoRR, 2023

Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics.

[BibT_eX]

[DOI]

Proceedings of the Quantitative Evaluation of Systems - 20th International Conference, 2023

Data-driven memory-dependent abstractions of dynamical systems.

[BibT_eX]

[DOI]

Proceedings of the Learning for Dynamics and Control Conference, 2023

Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding.

[BibT_eX]

[DOI]

Frederik Baymler Mathiesen

Ashish R. Hota

Alessandro Abate

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Inner Approximations of Stochastic Programs for Data-Driven Stochastic Barrier Function Design.

[BibT_eX]

[DOI]

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Abstracting Linear Stochastic Systems via Knowledge Filtering.

[BibT_eX]

[DOI]

Maico Hendrikus Wilhelmus Engelaar

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Data-driven Abstractions via Adaptive Refinements and a Kantorovich Metric.

[BibT_eX]

[DOI]

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics.

[BibT_eX]

[DOI]

CoRR, 2022

Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Scalable and data-driven approaches to convex programming

[BibT_eX]

[DOI]

PhD thesis, 2021

Subgradient averaging for multi-agent optimisation with different constraint sets.

[BibT_eX]

[DOI]

Giuseppe Notarstefano

Autom., 2021

Tight sampling and discarding bounds for scenario programs with an arbitrary number of removed samples.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

2020

Tight generalization guarantees for the sampling and discarding approach to scenario optimization.

[BibT_eX]

[DOI]

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Convergence rate analysis of a subgradient averaging algorithm for distributed optimisation with different constraint sets.

[BibT_eX]

[DOI]

Giuseppe Notarstefano

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

Distributed Actuator Selection: Achieving Optimality via a Primal-Dual Algorithm.

[BibT_eX]

[DOI]