Luca Laurenti

Orcid: 0000-0003-1190-6097

According to our database1, Luca Laurenti authored at least 60 papers between 2016 and 2024.

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Bibliography

2024
Formal Analysis of the Sampling Behavior of Stochastic Event-Triggered Control.
IEEE Trans. Autom. Control., July, 2024

Error Bounds for Deep Learning-based Uncertainty Propagation in SDEs.
CoRR, 2024

A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution.
CoRR, 2024

Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification.
CoRR, 2024

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection.
CoRR, 2024

Data-Driven Permissible Safe Control with Barrier Certificates.
CoRR, 2024

Piecewise Stochastic Barrier Functions.
CoRR, 2024

Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification.
CoRR, 2024

A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area.
CoRR, 2024

IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes.
CoRR, 2024

Probabilistic reach-avoid for Bayesian neural networks.
Artif. Intell., 2024

Data-driven strategy synthesis for stochastic systems with unknown nonlinear disturbances.
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

Adversarial Robustness Certification for Bayesian Neural Networks.
Proceedings of the Formal Methods - 26th International Symposium, 2024

2023
Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.
IEEE J. Biomed. Health Informatics, August, 2023

Formal Abstraction of General Stochastic Systems via Noise Partitioning.
IEEE Control. Syst. Lett., 2023

Promises of Deep Kernel Learning for Control Synthesis.
IEEE Control. Syst. Lett., 2023

Safety Certification for Stochastic Systems via Neural Barrier Functions.
IEEE Control. Syst. Lett., 2023

Unifying Safety Approaches for Stochastic Systems: From Barrier Functions to Uncertain Abstractions via Dynamic Programming.
CoRR, 2023

Individual Fairness in Bayesian Neural Networks.
CoRR, 2023

BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming.
Proceedings of the International Conference on Machine Learning, 2023

Distributionally Robust Strategy Synthesis for Switched Stochastic Systems.
Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control, 2023

Interval Markov Decision Processes with Continuous Action-Spaces.
Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control, 2023

Inner Approximations of Stochastic Programs for Data-Driven Stochastic Barrier Function Design.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
PID Control of Biochemical Reaction Networks.
IEEE Trans. Autom. Control., 2022

Adversarial Robustness Guarantees for Gaussian Processes.
J. Mach. Learn. Res., 2022

Formal Control Synthesis for Stochastic Neural Network Dynamic Models.
IEEE Control. Syst. Lett., 2022

On the Robustness of Bayesian Neural Networks to Adversarial Attacks.
CoRR, 2022

Formal Analysis of the Sampling Behaviour of Stochastic Event-Triggered Control.
CoRR, 2022

Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression.
CoRR, 2022

Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Individual Fairness Guarantees for Neural Networks.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Formal and Efficient Synthesis for Continuous-Time Linear Stochastic Hybrid Processes.
IEEE Trans. Autom. Control., 2021

A Language for Modeling and Optimizing Experimental Biological Protocols.
Comput., 2021

Certification of iterative predictions in Bayesian neural networks.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Strategy synthesis for partially-known switched stochastic systems.
Proceedings of the HSCC '21: 24th ACM International Conference on Hybrid Systems: Computation and Control, 2021

Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Bayesian Inference with Certifiable Adversarial Robustness.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Gradient-Free Adversarial Attacks for Bayesian Neural Networks.
CoRR, 2020

Probabilistic Safety for Bayesian Neural Networks.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Robustness of Bayesian Neural Networks to Gradient-Based Attacks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Assessing Robustness of Text Classification through Maximal Safe Radius Computation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Gaussian Processes with Physiologically-Inspired Priors for Physical Arousal Recognition.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

Safety Guarantees for Iterative Predictions with Gaussian Processes.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Adversarial Robustness Guarantees for Classification with Gaussian Processes.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Central Limit Model Checking.
ACM Trans. Comput. Log., 2019

Safety Guarantees for Planning Based on Iterative Gaussian Processes.
CoRR, 2019

Robustness Quantification for Classification with Gaussian Processes.
CoRR, 2019

Statistical Guarantees for the Robustness of Bayesian Neural Networks.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Efficiency through uncertainty: scalable formal synthesis for stochastic hybrid systems.
Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, 2019

Robustness Guarantees for Bayesian Inference with Gaussian Processes.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Programming discrete distributions with chemical reaction networks.
Nat. Comput., 2018

Experimental Biological Protocols with Formal Semantics.
Proceedings of the Computational Methods in Systems Biology, 2018

2017
Reachability Computation for Switching Diffusions: Finite Abstractions with Certifiable and Tuneable Precision.
Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control, 2017

Syntax-Guided Optimal Synthesis for Chemical Reaction Networks.
Proceedings of the Computer Aided Verification - 29th International Conference, 2017

2016
Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.
Biosyst., 2016

Approximation of Probabilistic Reachability for Chemical Reaction Networks Using the Linear Noise Approximation.
Proceedings of the Quantitative Evaluation of Systems - 13th International Conference, 2016

A Stochastic Hybrid Approximation for Chemical Kinetics Based on the Linear Noise Approximation.
Proceedings of the Computational Methods in Systems Biology, 2016


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