79 citations · 424 across the 54 of their papers we have counts for
65 papers
Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Sara Candussio, Gaia Saveri, Gabriele Sarti +1
Continuous representations of logic formulae allow us to integrate symbolic knowledge into data-driven learning algorithms. If such embeddings are semantically consistent, i.e. if…
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
Gaia Saveri, Laura Nenzi, Luca Bortolussi +1
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable…
Retrieval-Augmented Mining of Temporal Logic Specifications from Data
Gaia Saveri, Luca Bortolussi
The integration of cyber-physical systems (CPS) into everyday life raises the critical necessity of ensuring their safety and reliability. An important step in this direction is re…
ECATS: Explainable-by-design concept-based anomaly detection for time series
Irene Ferfoglia, Gaia Saveri, Laura Nenzi +1
Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection. However, the complexity…
Zero-Shot Conditioning of Score-Based Diffusion Models by Neuro-Symbolic Constraints
Davide Scassola, Sebastiano Saccani, Ginevra Carbone +1
Score-based diffusion models have emerged as effective approaches for both conditional and unconditional generation. Still conditional generation is based on either a specific trai…
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi
In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a s…
Towards Invertible Semantic-Preserving Embeddings of Logical Formulae
Gaia Saveri, Luca Bortolussi
Logic is the main formal language to perform automated reasoning, and it is further a human-interpretable language, at least for small formulae. Learning and optimising logic requi…
Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
Lorenzo Basile, Nikos Karantzas, Alberto d'Onofrio +4
Despite their impressive performance in classification tasks, neural networks are known to be vulnerable to adversarial attacks, subtle perturbations of the input data designed to…
Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
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. Due to its relevance for…
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Luca Bortolussi, Ginevra Carbone, Luca Laurenti +3
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical a…
Graph Neural Networks for Propositional Model Counting
Gaia Saveri, Luca Bortolussi
Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are…
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking
Luca Bortolussi, Francesca Cairoli, Ginevra Carbone +1
Parametric verification of linear temporal properties for stochastic models can be expressed as computing the satisfaction probability of a certain property as a function of the pa…
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský +1
We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and compu…
Variance Reduction in Stochastic Reaction Networks using Control Variates
Michael Backenköhler, Luca Bortolussi, Verena Wolf
Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However, reducing the statistical uncertainty of the corresponding estimators requires sampling a la…
Neural Predictive Monitoring under Partial Observability
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. We work under the most realistic settings w…
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Francesca Cairoli, Ginevra Carbone, Luca Bortolussi
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields. The associated Markov…
Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models
Michael Backenköhler, Luca Bortolussi, Gerrit Großmann +1
To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation…
MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties
Ezio Bartocci, Luca Bortolussi, Michele Loreti +2
We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile and spatially distributed cyber-physical systems (CPS). In the proposed framework, spa…
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations o…
Random Projections for Improved Adversarial Robustness
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool net…
Analysis of Markov Jump Processes under Terminal Constraints
Michael Backenköhler, Luca Bortolussi, Gerrit Großmann +1
Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We…
A kernel function for Signal Temporal Logic formulae
Luca Bortolussi, Giuseppe Maria Gallo, Laura Nenzi
We discuss how to define a kernel for Signal Temporal Logic (STL) formulae. Such a kernel allows us to embed the space of formulae into a Hilbert space, and opens up the use of ker…