32 citations · 43 across the 20 of their papers we have counts for
22 papers
Safety Certification is Classification
Oliver Schön, Licio Romao, Sadegh Soudjani
The goal of this paper is certifying safety of dynamical systems subject to uncertainty. Existing approaches use trajectory data to estimate transition probabilities, and compute s…
Truthful Production Uncertainty in Electricity Markets: A Two-Stage Mechanism
Shobhit Singhal, Lesia Mitridati, Licio Romao
Renewable power sources have low production costs, but may result in high balancing costs due to the inherent production uncertainty. Current day-ahead markets elicit only point pr…
Exact Recourse Functions for Aggregations of EVs Operating in Imbalance Markets
Karan Mukhi, Licio Romao, Alessandro Abate
We study optimal charging of large electric vehicle populations that are exposed to a single real-time imbalance price. The problem is naturally cast as a multistage stochastic lin…
Bridging conformal prediction and scenario optimization
Niall O'Sullivan, Licio Romao, Kostas Margellos
Conformal prediction and scenario optimization constitute two important classes of statistical learning frameworks to certify decisions made using data. They have found numerous ap…
Risk-Averse Certification of Bayesian Neural Networks
Xiyue Zhang, Zifan Wang, Yulong Gao +3
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In t…
A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the…
Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
Adrien Banse, Licio Romao, Alessandro Abate +1
Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven…
Data-driven Interval MDP for Robust Control Synthesis
Rudi Coppola, Andrea Peruffo, Licio Romao +2
The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework. We investigate a novel scheme to obtain…
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
Thom Badings, Licio Romao, Alessandro Abate +1
Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction meth…
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers
Alexandros E. Tzikas, Licio Romao, Mert Pilanci +2
Many machine learning applications require operating on a spatially distributed dataset. Despite technological advances, privacy considerations and communication constraints may pr…
Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions
Thom Badings, Nils Jansen, Licio Romao +1
Automated synthesis of correct-by-construction controllers for autonomous systems is crucial for their deployment in safety-critical scenarios. Such autonomous systems are naturall…
Abstracting Linear Stochastic Systems via Knowledge Filtering
Maico Hendrikus Wilhelmus Engelaar, Licio Romao, Yulong Gao +3
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This…
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
This paper proposes a new framework to compute finite-horizon safety guarantees for discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The appr…
Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding
Licio Romao, Ashish R. Hota, Alessandro Abate
We present a novel distributionally robust framework for dynamic programming that uses kernel methods to design feedback control policies. Specifically, we leverage kernel mean emb…
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
Adrien Banse, Licio Romao, Alessandro Abate +1
We introduce an adaptive refinement procedure for smart, and scalable abstraction of dynamical systems. Our technique relies on partitioning the state space depending on the observ…
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
Thom Badings, Licio Romao, Alessandro Abate +4
Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynam…
Data-driven memory-dependent abstractions of dynamical systems
Adrien Banse, Licio Romao, Alessandro Abate +1
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show th…
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
Luke Rickard, Thom Badings, Licio Romao +1
Automated synthesis of provably correct controllers for cyber-physical systems is crucial for deployment in safety-critical scenarios. However, hybrid features and stochastic or un…
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Thom Badings, Licio Romao, Alessandro Abate +1
Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of…
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives
Daniel Jarne Ornia, Licio Romao, Lewis Hammond +2
Policy robustness in Reinforcement Learning may not be desirable at any cost: the alterations caused by robustness requirements from otherwise optimal policies should be explainabl…
On the exact feasibility of convex scenario programs with discarded constraints
Licio Romao, Antonis Papachristodoulou, Kostas Margellos
We revisit the so-called sampling and discarding approach used to quantify the probability of constraint violation of a solution to convex scenario programs when some of the origin…
Subgradient averaging for multi-agent optimisation with different constraint sets
Licio Romao, Kostas Margellos, Giuseppe Notarstefano +1
We consider a multi-agent setting with agents exchanging information over a possibly time-varying network, aiming at minimising a separable objective function subject to constraint…