Search Results for author: Nicola Paoletti

Found 17 papers, 8 papers with code

Joint Prediction Regions for time-series models

no code implementations14 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).

Conformal Off-Policy Prediction for Multi-Agent Systems

no code implementations25 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.

Conformal Prediction

Counterfactual Influence in Markov Decision Processes

no code implementations13 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).

counterfactual Counterfactual Inference

Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees

no code implementations4 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.

Conformal Prediction Uncertainty Quantification

Causal Temporal Reasoning for Markov Decision Processes

no code implementations16 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).

counterfactual Counterfactual Reasoning +1

Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes

1 code implementation4 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.

Prediction Intervals

Neural Predictive Monitoring under Partial Observability

1 code implementation16 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.

Active Learning Conformal Prediction

Certification of Iterative Predictions in Bayesian Neural Networks

1 code implementation21 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.

Reinforcement Learning (RL)

On Guaranteed Optimal Robust Explanations for NLP Models

1 code implementation8 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).

Sentiment Analysis

Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues

no code implementations17 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.

Decision Making

MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

1 code implementation3 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.

Bayesian Inference Imitation Learning +1

A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

no code implementations31 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.

reinforcement-learning Reinforcement Learning (RL)

Neural Simplex Architecture

no code implementations1 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.

Continuous Control

Statistical Guarantees for the Robustness of Bayesian Neural Networks

1 code implementation5 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.

General Classification Image Classification

Neural State Classification for Hybrid Systems

1 code implementation26 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.

Classification General Classification

How to Learn a Model Checker

no code implementations5 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.

BIG-bench Machine Learning

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