41 citations · 68 across the 26 of their papers we have counts for
27 papers
Generalised Transportability via Causal Abstractions
Yorgos Felekis, Paris Giampouras, Fabio Massimo Zennaro +1
Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportability provides a criterion fo…
Teleological Inference in Structural Causal Models via Intentional Interventions
Dario Compagno, Fabio Massimo Zennaro
Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and answer teleological questions,…
Multi-Level Causal Embeddings
Willem Schooltink, Fabio Massimo Zennaro
Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two model…
Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu +2
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the inf…
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…
Aligning Graphical and Functional Causal Abstractions
Willem Schooltink, Fabio Massimo Zennaro
Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions def…
Causally Abstracted Multi-armed Bandits
Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer +4
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems. The majority of prior work typically studies and solves individual MAB and…
Interventionally Consistent Surrogates for Agent-based Simulators
Joel Dyer, Nicholas Bishop, Yorgos Felekis +4
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents. Their high-fidelit…
Causal Optimal Transport of Abstractions
Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini +1
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. T…
Quantifying Consistency and Information Loss for Causal Abstraction Learning
Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstracti…
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei +2
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency…
Towards Computing an Optimal Abstraction for Structural Causal Models
Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Working with causal models at different levels of abstraction is an important feature of science. Existing work has already considered the problem of expressing formally the relati…
Abstraction between Structural Causal Models: A Review of Definitions and Properties
Fabio Massimo Zennaro
Structural causal models (SCMs) are a widespread formalism to deal with causal systems. A recent direction of research has considered the problem of relating formally SCMs at diffe…
Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents
Laszlo Erdodi, Åvald Åslaugson Sommervoll, Fabio Massimo Zennaro
In this paper, we propose a formalization of the process of exploitation of SQL injection vulnerabilities. We consider a simplification of the dynamics of SQL injection attacks by…
Stack-based Buffer Overflow Detection using Recurrent Neural Networks
William Arild Dahl, Laszlo Erdodi, Fabio Massimo Zennaro
Detecting vulnerabilities in software is a critical challenge in the development and deployment of applications. One of the most known and dangerous vulnerabilities is stack-based…
Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions
Alexander Egiazarov, Fabio Massimo Zennaro, Vasileios Mavroeidis
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents such as terrorism, general crimin…
The Agent Web Model -- Modelling web hacking for reinforcement learning
Laszlo Erdodi, Fabio Massimo Zennaro
Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools use…
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
Fabio Massimo Zennaro, Audun Jøsang
The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation. Properly managing this t…
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge
Fabio Massimo Zennaro, Laszlo Erdodi
Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by…
Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks
Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro +1
In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get…
Towards Further Understanding of Sparse Filtering via Information Bottleneck
Fabio Massimo Zennaro, Ke Chen
In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in t…
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
Fabio Massimo Zennaro
In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bay…
Counterfactually Fair Prediction Using Multiple Causal Models
Fabio Massimo Zennaro, Magdalena Ivanovska
In this paper we study the problem of making predictions using multiple structural casual models defined by different agents, under the constraint that the prediction satisfies the…
An Empirical Evaluation of the Approximation of Subjective Logic Operators Using Monte Carlo Simulations
Fabio Massimo Zennaro, Magdalena Ivanovska, Audun Jøsang
In this paper we analyze the use of subjective logic as a framework for performing approximate transformations over probability distribution functions. As for any approximation, we…