no code implementations • 14 May 2024 • Eshant English, Nicola Paoletti

The method under study is based on bootstrapping and is applied to different datasets (Min Temp, Sunspots), using different predictors (e. g. ARIMA and LSTM).

no code implementations • 25 Mar 2024 • Tom Kuipers, Renukanandan Tumu, Shuo Yang, Milad Kazemi, Rahul Mangharam, Nicola Paoletti

In this work, we introduce MA-COPP, the first conformal prediction method to solve OPP problems involving multi-agent systems, deriving joint prediction regions for all agents' trajectories when one or more "ego" agents change their policies.

no code implementations • 13 Feb 2024 • Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler, Nicola Paoletti

Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs).

no code implementations • 4 Dec 2023 • Francesca Cairoli, Luca Bortolussi, Nicola Paoletti

This tutorial focuses on efficient methods to predictive monitoring (PM), the problem of detecting at runtime future violations of a given requirement from the current state of a system.

1 code implementation • 3 Oct 2023 • Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska

Such computed lower bounds provide safety certification for the given policy and BNN model.

no code implementations • 16 Dec 2022 • Milad Kazemi, Nicola Paoletti

We introduce $\textit{PCFTL (Probabilistic CounterFactual Temporal Logic)}$, a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP).

1 code implementation • 4 Nov 2022 • Francesca Cairoli, Nicola Paoletti, Luca Bortolussi

We consider the problem of predictive monitoring (PM), i. e., predicting at runtime the satisfaction of a desired property from the current system's state.

1 code implementation • 16 Aug 2021 • Francesca Cairoli, Luca Bortolussi, Nicola Paoletti

We consider the problem of predictive monitoring (PM), i. e., predicting at runtime future violations of a system from the current state.

1 code implementation • 21 May 2021 • Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska

We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models.

1 code implementation • 8 May 2021 • Emanuele La Malfa, Agnieszka Zbrzezny, Rhiannon Michelmore, Nicola Paoletti, Marta Kwiatkowska

We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP).

no code implementations • 17 Sep 2020 • Pallavi Bagga, Nicola Paoletti, Kostas Stathis

We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty.

1 code implementation • 3 Mar 2020 • Hongkai Chen, Nicola Paoletti, Scott A. Smolka, Shan Lin

Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices.

no code implementations • 31 Jan 2020 • Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets.

no code implementations • 1 Aug 2019 • Dung T. Phan, Radu Grosu, Nils Jansen, Nicola Paoletti, Scott A. Smolka, Scott D. Stoller

NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance.

1 code implementation • 5 Mar 2019 • Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, Matthew Wicker

We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two.

1 code implementation • 26 Jul 2018 • Dung Phan, Nicola Paoletti, Timothy Zhang, Radu Grosu, Scott A. Smolka, Scott D. Stoller

We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique.

no code implementations • 5 Dec 2017 • Dung Phan, Radu Grosu, Nicola Paoletti, Scott A. Smolka, Scott D. Stoller

We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model trajectories.

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