no code implementations • 26 Apr 2024 • Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu, Michael Wooldridge, Theodoros Damoulas
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems.
no code implementations • 18 Dec 2023 • Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Anisoara Calinescu, Theodoros Damoulas, Michael Wooldridge
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.
1 code implementation • 13 Dec 2023 • Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them.
1 code implementation • 7 May 2023 • Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models.
1 code implementation • 14 Jan 2023 • Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, W. Dhammika Widanage, Theodoros Damoulas
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution.
1 code implementation • 1 Aug 2022 • Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Working with causal models at different levels of abstraction is an important feature of science.
no code implementations • 18 Jul 2022 • Fabio Massimo Zennaro
Structural causal models (SCMs) are a widespread formalism to deal with causal systems.
1 code implementation • 8 Jan 2021 • Laszlo Erdodi, Åvald Åslaugson Sommervoll, Fabio Massimo Zennaro
In this paper, we propose a formalization of the process of exploitation of SQL injection vulnerabilities.
1 code implementation • 30 Dec 2020 • William Arild Dahl, Laszlo Erdodi, Fabio Massimo Zennaro
Moreover, we subscribe to the hypothesis that code may be treated as natural language, and thus we process assembly code using standard architectures commonly employed in natural language processing.
no code implementations • 17 Dec 2020 • 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 criminal offences, or even domestic violence.
1 code implementation • 17 Aug 2020 • 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.
1 code implementation • 26 May 2020 • Fabio Massimo Zennaro, Laszlo Erdodi
In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them.
no code implementations • 11 Feb 2020 • Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro, Kamer Vishi
In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon.
1 code implementation • 20 Oct 2019 • 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 the study of the information bottleneck (IB).
no code implementations • 20 Jun 2019 • Fabio Massimo Zennaro
In this paper we offer a preliminary study of the application of Bayesian coresets to network security data.
1 code implementation • 1 Oct 2018 • 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 criterion of counterfactual fairness.
no code implementations • 24 May 2018 • Fabio Massimo Zennaro, Magdalena Ivanovska
In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness.
1 code implementation • 22 Jul 2016 • Fabio Massimo Zennaro, Ke Chen
We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation.
no code implementations • 29 Mar 2016 • Fabio Massimo Zennaro, Ke Chen
In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning.