Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Bacciu, Davide; Numeroso, Danilo Explaining Deep Graph Networks via Input Perturbation Journal Article In: IEEE Transactions on Neural Networks and Learning Systems, 2022. Crecchi, Francesco; Melis, Marco; Sotgiu, Angelo; Bacciu, Davide; Biggio, Battista FADER: Fast Adversarial Example Rejection Journal Article In: Neurocomputing, 2021, ISSN: 0925-2312.
@article{Bacciu2022,
title = {Explaining Deep Graph Networks via Input Perturbation},
author = {Davide Bacciu and Danilo Numeroso
},
doi = {10.1109/TNNLS.2022.3165618},
year = {2022},
date = {2022-04-21},
urldate = {2022-04-21},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
abstract = {Deep Graph Networks are a family of machine learning models for structured data which are finding heavy application in life-sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The privacy and safety-critical nature of such domains motivates the need for developing effective explainability methods for this family of models. So far, progress in this field has been challenged by the combinatorial nature and complexity of graph structures. In this respect, we present a novel local explanation framework specifically tailored to graph data and deep graph networks. Our approach leverages reinforcement learning to generate meaningful local perturbations of the input graph, whose prediction we seek an interpretation for. These perturbed data points are obtained by optimising a multi-objective score taking into account similarities both at a structural level as well as at the level of the deep model outputs. By this means, we are able to populate a set of informative neighbouring samples for the query graph, which is then used to fit an interpretable model for the predictive behaviour of the deep network locally to the query graph prediction. We show the effectiveness of the proposed explainer by a qualitative analysis on two chemistry datasets, TOS and ESOL and by quantitative results on a benchmark dataset for explanations, CYCLIQ.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{CRECCHI2021,
title = {FADER: Fast Adversarial Example Rejection},
author = {Francesco Crecchi and Marco Melis and Angelo Sotgiu and Davide Bacciu and Battista Biggio},
url = {https://arxiv.org/abs/2010.09119, Arxiv},
doi = {https://doi.org/10.1016/j.neucom.2021.10.082},
issn = {0925-2312},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Neurocomputing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}