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.
Ninniri, Matteo; Podda, Marco; Bacciu, Davide Classifier-free graph diffusion for molecular property targeting Workshop 4th workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR) at AAAI 2024, 2024. Landolfi, Francesco; Bacciu, Davide; Numeroso, Danilo A Tropical View of Graph Neural Networks Conference Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , 2023. Bacciu, Davide; Errica, Federico; Navarin, Nicolò; Pasa, Luca; Zambon, Daniele Deep Learning for Graphs Conference Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022), 2022. Serramazza, Davide Italo; Bacciu, Davide Learning image captioning as a structured transduction task Conference Proceedings of the 23rd International Conference on Engineering Applications of Neural Networks (EANN 2022), vol. 1600, Communications in Computer and Information Science Springer, 2022. Bacciu, Davide; Numeroso, Danilo Explaining Deep Graph Networks via Input Perturbation Journal Article In: IEEE Transactions on Neural Networks and Learning Systems, 2022. Gravina, Alessio; Wilson, Jennifer L.; Bacciu, Davide; Grimes, Kevin J.; Priami, Corrado Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks Journal Article In: Plos Computational Biology, vol. 18, no. 5, 2022. Castellana, Daniele; Bacciu, Davide A Tensor Framework for Learning in Structured Domains Journal Article In: Neurocomputing, vol. 470, pp. 405-426, 2022. Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide Catastrophic Forgetting in Deep Graph Networks: a Graph Classification benchmark Journal Article In: Frontiers in Artificial Intelligence , 2022. Bacciu, Davide; Bianchi, Filippo Maria; Paassen, Benjamin; Alippi, Cesare Deep learning for graphs Conference Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), 2021. Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea K-Plex Cover Pooling for Graph Neural Networks Journal Article In: Data Mining and Knowledge Discovery, 2021, (Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)). Atzeni, Daniele; Bacciu, Davide; Errica, Federico; Micheli, Alessio Modeling Edge Features with Deep Bayesian Graph Networks Conference Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE IEEE, 2021. Numeroso, Danilo; Bacciu, Davide MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks Conference Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE 2021. Errica, Federico; Bacciu, Davide; Micheli, Alessio Graph Mixture Density Networks Conference Proceedings of the 38th International Conference on Machine Learning (ICML 2021), PMLR, 2021. Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification Workshop The Web Conference 2021 Workshop on Graph Learning Benchmarks (GLB21), 2021. Errica, Federico; Giulini, Marco; Bacciu, Davide; Menichetti, Roberto; Micheli, Alessio; Potestio, Raffaello A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins Journal Article In: Frontiers in Molecular Biosciences, vol. 8, pp. 136, 2021. Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea K-plex Cover Pooling for Graph Neural Networks Workshop 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020. Bacciu, Davide; Numeroso, Danilo Explaining Deep Graph Networks with Molecular Counterfactuals Workshop 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020. Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco A Gentle Introduction to Deep Learning for Graphs Journal Article In: Neural Networks, vol. 129, pp. 203-221, 2020. Bacciu, Davide; Errica, Federico; Micheli, Alessio Probabilistic Learning on Graphs via Contextual Architectures Journal Article In: Journal of Machine Learning Research, vol. 21, no. 134, pp. 1−39, 2020. Castellana, Daniele; Bacciu, Davide Generalising Recursive Neural Models by Tensor Decomposition Conference Proceedings of the 2020 IEEE World Congress on Computational Intelligence, 2020. Podda, Marco; Bacciu, Davide; Micheli, Alessio A Deep Generative Model for Fragment-Based Molecule Generation Conference Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) , 2020. Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio A Fair Comparison of Graph Neural Networks for Graph Classification Conference Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020), 2020. Podda, Marco; Micheli, Alessio; Bacciu, Davide; Milazzo, Paolo Biochemical Pathway Robustness Prediction with Graph Neural Networks Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020. Errica, Federico; Bacciu, Davide; Micheli, Alessio Theoretically Expressive and Edge-aware Graph Learning Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020. Bacciu, Davide; Mandic, Danilo Tensor Decompositions in Deep Learning Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020. Castellana, Daniele; Bacciu, Davide Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020. Bacciu, Davide; Micheli, Alessio Deep Learning for Graphs Book Chapter In: Oneto, Luca; Navarin, Nicolo; Sperduti, Alessandro; Anguita, Davide (Ed.): Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019), vol. 896, pp. 99-127, Springer International Publishing, 2020, ISBN: 978-3-030-43883-8. Bacciu, Davide; Micheli, Alessio; Podda, Marco Edge-based sequential graph generation with recurrent neural networks Journal Article In: Neurocomputing, 2019. Bacciu, Davide; Sotto, Luigi Di A non-negative factorization approach to node pooling in graph convolutional neural networks Conference Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019), Lecture Notes in Artificial Intelligence Springer-Verlag, 2019. Castellana, Daniele; Bacciu, Davide Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models Conference Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019I) , IEEE, 2019. Davide, Bacciu; Daniele, Castellana Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering Journal Article In: Neurocomputing, vol. 342, pp. 49-59, 2019, ISBN: 0925-2312. Bacciu, Davide; Micheli, Alessio; Podda, Marco Graph generation by sequential edge prediction Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'19), i6doc.com, Louvain-la-Neuve, Belgium, 2019. Davide, Bacciu; Antonio, Bruno Deep Tree Transductions - A Short Survey Conference Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) , Recent Advances in Big Data and Deep Learning Springer International Publishing, 2019. Davide, Bacciu; Antonio, Bruno Text Summarization as Tree Transduction by Top-Down TreeLSTM Conference Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018. Davide, Bacciu; Daniele, Castellana Learning Tree Distributions by Hidden Markov Models Workshop Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18), 2018. Davide, Bacciu; Federico, Errica; Alessio, Micheli Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing Conference Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018. Davide, Bacciu; Daniele, Castellana Mixture of Hidden Markov Models as Tree Encoder Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18), i6doc.com, Louvain-la-Neuve, Belgium, 2018, ISBN: 978-287587047-6. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Generative Kernels for Tree-Structured Data Journal Article In: Neural Networks and Learning Systems, IEEE Transactions on, 2018, ISSN: 2162-2388 . Davide, Bacciu Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data Conference Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17), IEEE, 2017. Davide, Bacciu Unsupervised feature selection for sensor time-series in pervasive computing applications Journal Article In: Neural Computing and Applications, vol. 27, no. 5, pp. 1077-1091, 2016, ISSN: 1433-3058. Davide, Bacciu; Vincenzo, Gervasi; Giuseppe, Prencipe An Investigation into Cybernetic Humor, or: Can Machines Laugh? Conference Proceedings of the 8th International Conference on Fun with Algorithms (FUN'16) , vol. 49, Leibniz International Proceedings in Informatics (LIPIcs) Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2016, ISSN: 1868-8969. Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Alessio, Micheli; Arantxa, Renteria; Claudio, Vairo A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living Conference Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16), vol. 476, Advances in Intelligent Systems and Computing Springer, 2016, ISBN: 978-3-319-40113-3. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli A reservoir activation kernel for trees Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16), i6doc.com, 2016, ISBN: 978-287587027-. Davide, Bacciu; Filippo, Benedetti; Alessio, Micheli ESNigma: efficient feature selection for Echo State Networks Conference Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), i6doc.com publ., 2015. Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Martin, McGinnity T; Alessio, Micheli; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo; Philip, Vance Robotic Ubiquitous Cognitive Ecology for Smart Homes Journal Article In: Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 57-81, 2015, ISSN: 0921-0296. Davide, Bacciu An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications Conference Communications in Computer and Information Science - Engineering Applications of Neural Networks, vol. 459, Springer International Publishing, 2014. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Integrating bi-directional contexts in a generative kernel for trees Conference Neural Networks (IJCNN), 2014 International Joint Conference on, IEEE, 2014. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti An input–output hidden Markov model for tree transductions Journal Article In: Neurocomputing, vol. 112, pp. 34–46, 2013, ISSN: 0925-2312. Davide, Bacciu; Stefano, CHESSA; Claudio, Gallicchio; Alessio, MICHELI; Paolo, Barsocchi An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living Conference Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets, vol. 19, Springer, 2013. Nicola, Di Mauro; Paolo, Frasconi; Fabrizio, Angiulli; Davide, Bacciu; de Gemmis Marco,; Floriana, Esposito; Nicola, Fanizzi; Stefano, Ferilli; Marco, Gori; A, Lisi Francesca; others, Italian Machine Learning and Data Mining research: The last years Journal Article In: Intelligenza Artificiale, vol. 7, no. 2, pp. 77–89, 2013.
@workshop{Ninniri2024,
title = {Classifier-free graph diffusion for molecular property targeting},
author = {Matteo Ninniri and Marco Podda and Davide Bacciu},
url = {https://arxiv.org/abs/2312.17397, Arxiv},
year = {2024},
date = {2024-02-27},
booktitle = {4th workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR) at AAAI 2024},
abstract = {This work focuses on the task of property targeting: that is, generating molecules conditioned on target chemical properties to expedite candidate screening for novel drug and materials development. DiGress is a recent diffusion model for molecular graphs whose distinctive feature is allowing property targeting through classifier-based (CB) guidance. While CB guidance may work to generate molecular-like graphs, we hint at the fact that its assumptions apply poorly to the chemical domain. Based on this insight we propose a classifier-free DiGress (FreeGress), which works by directly injecting the conditioning information into the training process. CF guidance is convenient given its less stringent assumptions and since it does not require to train an auxiliary property regressor, thus halving the number of trainable parameters in the model. We empirically show that our model yields up to 79% improvement in Mean Absolute Error with respect to DiGress on property targeting tasks on QM9 and ZINC-250k benchmarks. As an additional contribution, we propose a simple yet powerful approach to improve chemical validity of generated samples, based on the observation that certain chemical properties such as molecular weight correlate with the number of atoms in molecules. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@conference{Landolfi2023,
title = { A Tropical View of Graph Neural Networks },
author = {Francesco Landolfi and Davide Bacciu and Danilo Numeroso
},
editor = {Michel Verleysen},
year = {2023},
date = {2023-10-04},
urldate = {2023-10-04},
booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{nokey,
title = {Deep Learning for Graphs},
author = {Davide Bacciu and Federico Errica and Nicolò Navarin and Luca Pasa and Daniele Zambon},
editor = {Michel Verleysen},
year = {2022},
date = {2022-10-05},
urldate = {2022-10-05},
booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Serramazza2022,
title = {Learning image captioning as a structured transduction task},
author = {Davide Italo Serramazza and Davide Bacciu},
doi = {doi.org/10.1007/978-3-031-08223-8_20},
year = {2022},
date = {2022-06-20},
urldate = {2022-06-20},
booktitle = {Proceedings of the 23rd International Conference on Engineering Applications of Neural Networks (EANN 2022)},
volume = {1600},
pages = {235–246},
publisher = {Springer},
series = {Communications in Computer and Information Science },
abstract = {Image captioning is a task typically approached by deep encoder-decoder architectures, where the encoder component works on a flat representation of the image while the decoder considers a sequential representation of natural language sentences. As such, these encoder-decoder architectures implement a simple and very specific form of structured transduction, that is a generalization of a predictive problem where the input data and output predictions might have substantially different structures and topologies. In this paper, we explore a generalization of such an approach by addressing the problem as a general structured transduction problem. In particular, we provide a framework that allows considering input and output information with a tree-structured representation. This allows taking into account the hierarchical nature underlying both images and sentences. To this end, we introduce an approach to generate tree-structured representations from images along with an autoencoder working with this kind of data. We empirically assess our approach on both synthetic and realistic tasks.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@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{Gravina2022,
title = {Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks},
author = {Alessio Gravina and Jennifer L. Wilson and Davide Bacciu and Kevin J. Grimes and Corrado Priami},
url = {https://www.biorxiv.org/content/10.1101/2021.10.07.463459v1, BioArxiv},
doi = {doi.org/10.1371/journal.pcbi.1009531},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
journal = {Plos Computational Biology},
volume = {18},
number = {5},
abstract = {Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, ie reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, ie prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Castellana2021,
title = {A Tensor Framework for Learning in Structured Domains},
author = {Daniele Castellana and Davide Bacciu},
editor = {Kerstin Bunte and Niccolo Navarin and Luca Oneto},
doi = {10.1016/j.neucom.2021.05.110},
year = {2022},
date = {2022-01-22},
urldate = {2022-01-22},
journal = {Neurocomputing},
volume = {470},
pages = {405-426},
abstract = {Learning machines for structured data (e.g., trees) are intrinsically based on their capacity to learn representations by aggregating information from the multi-way relationships emerging from the structure topology. While complex aggregation functions are desirable in this context to increase the expressiveness of the learned representations, the modelling of higher-order interactions among structure constituents is unfeasible, in practice, due to the exponential number of parameters required. Therefore, the common approach is to define models which rely only on first-order interactions among structure constituents.
In this work, we leverage tensors theory to define a framework for learning in structured domains. Such a framework is built on the observation that more expressive models require a tensor parameterisation. This observation is the stepping stone for the application of tensor decompositions in the context of recursive models. From this point of view, the advantage of using tensor decompositions is twofold since it allows limiting the number of model parameters while injecting inductive biases that do not ignore higher-order interactions.
We apply the proposed framework on probabilistic and neural models for structured data, defining different models which leverage tensor decompositions. The experimental validation clearly shows the advantage of these models compared to first-order and full-tensorial models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we leverage tensors theory to define a framework for learning in structured domains. Such a framework is built on the observation that more expressive models require a tensor parameterisation. This observation is the stepping stone for the application of tensor decompositions in the context of recursive models. From this point of view, the advantage of using tensor decompositions is twofold since it allows limiting the number of model parameters while injecting inductive biases that do not ignore higher-order interactions.
We apply the proposed framework on probabilistic and neural models for structured data, defining different models which leverage tensor decompositions. The experimental validation clearly shows the advantage of these models compared to first-order and full-tensorial models.
@article{Carta2022,
title = {Catastrophic Forgetting in Deep Graph Networks: a Graph Classification benchmark},
author = {Antonio Carta and Andrea Cossu and Federico Errica and Davide Bacciu},
doi = {10.3389/frai.2022.824655},
year = {2022},
date = {2022-01-11},
urldate = {2022-01-11},
journal = {Frontiers in Artificial Intelligence },
abstract = { In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{Bacciu2021c,
title = { Deep learning for graphs},
author = {Davide Bacciu and Filippo Maria Bianchi and Benjamin Paassen and Cesare Alippi},
editor = {Michel Verleysen},
doi = {10.14428/esann/2021.ES2021-5},
year = {2021},
date = {2021-10-06},
urldate = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)},
pages = {89-98},
abstract = { Deep learning for graphs encompasses all those models endowed with multiple layers of abstraction, which operate on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph based on a sum of messages received from its neighbors. However, the family also includes architectures with decoders from vectors to graphs and models that process time-varying graphs and hypergraphs. In this paper, we provide an overview of the key concepts in the field, point towards open questions, and frame the contributions of the ESANN 2021 special session into the broader context of deep learning for graphs. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{Bacciu2021b,
title = {K-Plex Cover Pooling for Graph Neural Networks},
author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},
editor = {Annalisa Appice and Sergio Escalera and José A. Gámez and Heike Trautmann},
url = {https://link.springer.com/article/10.1007/s10618-021-00779-z, Published version},
doi = {10.1007/s10618-021-00779-z},
year = {2021},
date = {2021-09-13},
urldate = {2021-09-13},
journal = {Data Mining and Knowledge Discovery},
abstract = {raph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool, builds on the concepts of graph covers and k-plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.},
note = {Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{Atzeni2021,
title = { Modeling Edge Features with Deep Bayesian Graph Networks},
author = {Daniele Atzeni and Davide Bacciu and Federico Errica and Alessio Micheli},
doi = {10.1109/IJCNN52387.2021.9533430},
year = {2021},
date = {2021-07-18},
urldate = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
publisher = {IEEE},
organization = {IEEE},
abstract = {We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an additional Bayesian network mapping edge features into discrete states to be used by the original model. In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks. Moreover, we successfully test our proposal in a graph regression scenario where edge features are of fundamental importance, and we show that the learned edge representation provides substantial performance improvements against the original model on three link prediction tasks. By keeping the computational complexity linear in the number of edges, the proposed model is amenable to large-scale graph processing.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Numeroso2021,
title = {MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks},
author = {Danilo Numeroso and Davide Bacciu},
year = {2021},
date = {2021-07-18},
urldate = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Errica2021,
title = {Graph Mixture Density Networks},
author = {Federico Errica and Davide Bacciu and Alessio Micheli},
url = {https://proceedings.mlr.press/v139/errica21a.html, PDF},
year = {2021},
date = {2021-07-18},
urldate = {2021-07-18},
booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML 2021)},
pages = {3025-3035},
publisher = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@workshop{Carta2021,
title = { Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification },
author = {Antonio Carta and Andrea Cossu and Federico Errica and Davide Bacciu},
year = {2021},
date = {2021-04-12},
urldate = {2021-04-12},
booktitle = {The Web Conference 2021 Workshop on Graph Learning Benchmarks (GLB21)},
abstract = {In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{errica_deep_2021,
title = {A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins},
author = {Federico Errica and Marco Giulini and Davide Bacciu and Roberto Menichetti and Alessio Micheli and Raffaello Potestio},
doi = {10.3389/fmolb.2021.637396},
year = {2021},
date = {2021-02-28},
urldate = {2021-02-28},
journal = {Frontiers in Molecular Biosciences},
volume = {8},
pages = {136},
publisher = {Frontiers},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@workshop{kplexWS2020,
title = {K-plex Cover Pooling for Graph Neural Networks},
author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},
year = {2020},
date = {2020-12-11},
urldate = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms},
abstract = {We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links. The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@workshop{megWS2020,
title = {Explaining Deep Graph Networks with Molecular Counterfactuals},
author = {Davide Bacciu and Danilo Numeroso},
url = {https://arxiv.org/pdf/2011.05134.pdf, Arxiv},
year = {2020},
date = {2020-12-11},
urldate = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral)},
abstract = {We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator). We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties. We discuss preliminary results showing how the model can convey non-ML experts with key insights into the learning model focus in the neighborhood of a molecule. },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{gentleGraphs2020,
title = {A Gentle Introduction to Deep Learning for Graphs},
author = {Davide Bacciu and Federico Errica and Alessio Micheli and Marco Podda},
url = {https://arxiv.org/abs/1912.12693, Arxiv
https://doi.org/10.1016/j.neunet.2020.06.006, Original Paper},
doi = {10.1016/j.neunet.2020.06.006},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
journal = {Neural Networks},
volume = {129},
pages = {203-221},
publisher = {Elsevier},
abstract = {The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{jmlrCGMM20,
title = {Probabilistic Learning on Graphs via Contextual Architectures},
author = {Davide Bacciu and Federico Errica and Alessio Micheli},
editor = {Pushmeet Kohli},
url = {http://jmlr.org/papers/v21/19-470.html, Paper},
year = {2020},
date = {2020-07-27},
urldate = {2020-07-27},
journal = {Journal of Machine Learning Research},
volume = {21},
number = {134},
pages = {1−39},
abstract = {We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighborhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices.
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature.@conference{Wcci20Tensor,
title = {Generalising Recursive Neural Models by Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
url = {https://arxiv.org/abs/2006.10021, Arxiv},
year = {2020},
date = {2020-07-19},
urldate = {2020-07-19},
booktitle = {Proceedings of the 2020 IEEE World Congress on Computational Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{aistats2020,
title = {A Deep Generative Model for Fragment-Based Molecule Generation},
author = {Marco Podda and Davide Bacciu and Alessio Micheli},
url = {https://arxiv.org/abs/2002.12826},
year = {2020},
date = {2020-06-03},
urldate = {2020-06-03},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) },
abstract = {Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learn their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid or duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based clustering strategy that helps to generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{iclr19,
title = {A Fair Comparison of Graph Neural Networks for Graph Classification},
author = {Federico Errica and Marco Podda and Davide Bacciu and Alessio Micheli},
url = {https://openreview.net/pdf?id=HygDF6NFPB, PDF
https://iclr.cc/virtual_2020/poster_HygDF6NFPB.html, Talk
https://github.com/diningphil/gnn-comparison, Code},
year = {2020},
date = {2020-04-30},
booktitle = {Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020)},
abstract = {Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.
As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
@conference{esann20Podda,
title = { Biochemical Pathway Robustness Prediction with Graph Neural Networks },
author = {Marco Podda and Alessio Micheli and Davide Bacciu and Paolo Milazzo},
editor = {Michel Verleysen},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann20Errica,
title = { Theoretically Expressive and Edge-aware Graph Learning },
author = {Federico Errica and Davide Bacciu and Alessio Micheli},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2001.09005},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
abstract = {We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann20Tutorial,
title = {Tensor Decompositions in Deep Learning},
author = {Davide Bacciu and Danilo Mandic},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2002.11835},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann20Castellana,
title = { Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data },
author = {Daniele Castellana and Davide Bacciu},
editor = {Michel Verleysen},
url = {https://arxiv.org/pdf/2006.10619.pdf, Arxiv},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@inbook{graphsBDDL2020,
title = {Deep Learning for Graphs},
author = {Davide Bacciu and Alessio Micheli},
editor = {Luca Oneto and Nicolo Navarin and Alessandro Sperduti and Davide Anguita
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-43883-8_5},
doi = {10.1007/978-3-030-43883-8_5},
isbn = {978-3-030-43883-8},
year = {2020},
date = {2020-04-04},
booktitle = {Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)},
volume = {896},
pages = {99-127},
publisher = {Springer International Publishing},
series = {Studies in Computational Intelligence Series},
abstract = {We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{neucompEsann19,
title = {Edge-based sequential graph generation with recurrent neural networks},
author = {Davide Bacciu and Alessio Micheli and Marco Podda},
url = {https://arxiv.org/abs/2002.00102v1},
year = {2019},
date = {2019-12-31},
journal = {Neurocomputing},
abstract = { Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions, while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{aiia2019,
title = {A non-negative factorization approach to node pooling in graph convolutional neural networks},
author = {Davide Bacciu and Luigi {Di Sotto}},
url = {https://arxiv.org/pdf/1909.03287.pdf},
year = {2019},
date = {2019-11-22},
booktitle = {Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019)},
publisher = {Springer-Verlag},
series = {Lecture Notes in Artificial Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{ijcnn2019,
title = {Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models},
author = {Daniele Castellana and Davide Bacciu},
url = {https://arxiv.org/pdf/1905.13528.pdf},
year = {2019},
date = {2019-07-15},
urldate = {2019-07-15},
booktitle = {Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019I) },
publisher = {IEEE},
abstract = {Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-transition matrix. We propose a new approximation which lies on the Tucker factorisation of tensors. The probabilistic interpretation of such approximation allows us to define a new probabilistic model for tree-structured data. Hence, we define the new approximated model and we derive its learning algorithm. Then, we empirically assess the effective power of the new model evaluating it on two different tasks. In both cases, our model outperforms the other approximated model known in the literature.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{neucomBayesHTMM,
title = {Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering},
author = {Bacciu Davide and Castellana Daniele},
url = {https://doi.org/10.1016/j.neucom.2018.11.091},
doi = {10.1016/j.neucom.2018.11.091},
isbn = {0925-2312},
year = {2019},
date = {2019-05-21},
journal = {Neurocomputing},
volume = {342},
pages = {49-59},
abstract = {The paper deals with the problem of unsupervised learning with structured data, proposing a mixture model approach to cluster tree samples. First, we discuss how to use the Switching-Parent Hidden Tree Markov Model, a compositional model for learning tree distributions, to define a finite mixture model where the number of components is fixed by a hyperparameter. Then, we show how to relax such an assumption by introducing a Bayesian non-parametric mixture model where the number of necessary hidden tree components is learned from data. Experimental validation on synthetic and real datasets show the benefit of mixture models over simple hidden tree models in clustering applications. Further, we provide a characterization of the behaviour of the two mixture models for different choices of their hyperparameters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{esann19GraphGen,
title = {Graph generation by sequential edge prediction},
author = {Davide Bacciu and Alessio Micheli and Marco Podda},
editor = {Michel Verleysen},
url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-107.pdf},
year = {2019},
date = {2019-04-24},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'19)},
publisher = {i6doc.com},
address = {Louvain-la-Neuve, Belgium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{inns2019,
title = {Deep Tree Transductions - A Short Survey},
author = {Bacciu Davide and Bruno Antonio},
editor = {Luca Oneto and Nicol{`o} Navarin and Alessandro Sperduti and Davide Anguita},
url = {https://arxiv.org/abs/1902.01737},
doi = {10.1007/978-3-030-16841-4_25},
year = {2019},
date = {2019-01-04},
urldate = {2019-01-04},
booktitle = {Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) },
pages = {236--245},
publisher = {Springer International Publishing},
series = {Recent Advances in Big Data and Deep Learning},
abstract = {The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{ssci2018,
title = {Text Summarization as Tree Transduction by Top-Down TreeLSTM},
author = {Bacciu Davide and Bruno Antonio},
url = {https://arxiv.org/abs/1809.09096},
doi = {10.1109/SSCI.2018.8628873},
year = {2018},
date = {2018-11-18},
urldate = {2018-11-18},
booktitle = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18)},
pages = {1411-1418},
publisher = {IEEE},
abstract = {Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@workshop{learnaut18,
title = {Learning Tree Distributions by Hidden Markov Models},
author = {Bacciu Davide and Castellana Daniele},
editor = {Rémi Eyraud and Jeffrey Heinz and Guillaume Rabusseau and Matteo Sammartino },
url = {https://arxiv.org/abs/1805.12372},
year = {2018},
date = {2018-07-13},
booktitle = {Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@conference{icml2018,
title = {Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing},
author = {Bacciu Davide and Errica Federico and Micheli Alessio},
url = {https://arxiv.org/abs/1805.10636},
year = {2018},
date = {2018-07-11},
urldate = {2018-07-11},
booktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML 2018)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann2018Tree,
title = {Mixture of Hidden Markov Models as Tree Encoder},
author = {Bacciu Davide and Castellana Daniele},
editor = {Michel Verleysen},
isbn = {978-287587047-6},
year = {2018},
date = {2018-04-26},
urldate = {2018-04-26},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18)},
pages = {543-548},
publisher = {i6doc.com},
address = {Louvain-la-Neuve, Belgium},
abstract = {The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{tnnlsTreeKer17,
title = {Generative Kernels for Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/TNNLS.2017.2785292},
issn = {2162-2388 },
year = {2018},
date = {2018-01-15},
journal = {Neural Networks and Learning Systems, IEEE Transactions on},
abstract = {The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{dl2017,
title = {Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data},
author = {Bacciu Davide},
url = {https://arxiv.org/abs/1711.07784},
year = {2017},
date = {2017-11-27},
urldate = {2017-11-27},
booktitle = {Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{icfNca15,
title = {Unsupervised feature selection for sensor time-series in pervasive computing applications},
author = {Bacciu Davide},
url = {https://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/nca2015.pdf},
doi = {10.1007/s00521-015-1924-x},
issn = {1433-3058},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Neural Computing and Applications},
volume = {27},
number = {5},
pages = {1077-1091},
publisher = {Springer London},
abstract = {The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{fun2016,
title = {An Investigation into Cybernetic Humor, or: Can Machines Laugh?},
author = {Bacciu Davide and Gervasi Vincenzo and Prencipe Giuseppe},
editor = {Erik D. Demaine and Fabrizio Grandoni},
url = {http://drops.dagstuhl.de/opus/volltexte/2016/5882},
doi = {10.4230/LIPIcs.FUN.2016.3},
issn = {1868-8969},
year = {2016},
date = {2016-06-10},
booktitle = {Proceedings of the 8th International Conference on Fun with Algorithms (FUN'16) },
volume = {49},
pages = {1-15},
publisher = {Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
abstract = {The mechanisms of humour have been the subject of much study and investigation, starting with and up to our days. Much of this work is based on literary theories, put forward by some of the most eminent philosophers and thinkers of all times, or medical theories, investigating the impact of humor on brain activity or behaviour. Recent functional neuroimaging studies, for instance, have investigated the process of comprehending and appreciating humor by examining functional activity in distinctive regions of brains stimulated by joke corpora. Yet, there is precious little work on the computational side, possibly due to the less hilarious nature of computer scientists as compared to men of letters and sawbones. In this paper, we set to investigate whether literary theories of humour can stand the test of algorithmic laughter. Or, in other words, we ask ourselves the vexed question: Can machines laugh? We attempt to answer that question by testing whether an algorithm - namely, a neural network - can "understand" humour, and in particular whether it is possible to automatically identify abstractions that are predicted to be relevant by established literary theories about the mechanisms of humor. Notice that we do not focus here on distinguishing humorous from serious statements - a feat that is clearly way beyond the capabilities of the average human voter, not to mention the average machine - but rather on identifying the underlying mechanisms and triggers that are postulated to exist by literary theories, by verifying if similar mechanisms can be learned by machines. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Amato2016,
title = {A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living},
author = {Amato Giuseppe and Bacciu Davide and Chessa Stefano and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Micheli Alessio and Renteria Arantxa
and Vairo Claudio},
doi = {10.1007/978-3-319-40114-0_1},
isbn = {978-3-319-40113-3},
year = {2016},
date = {2016-06-03},
booktitle = {Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16)},
volume = {476},
pages = {1-9},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
abstract = {We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{esann2016,
title = {A reservoir activation kernel for trees},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio
},
editor = {M. Verleysen},
url = {https://www.researchgate.net/profile/Claudio_Gallicchio/publication/313236954_A_Reservoir_Activation_Kernel_for_Trees/links/58a9db0892851cf0e3c6b8df/A-Reservoir-Activation-Kernel-for-Trees.pdf},
isbn = {978-287587027-},
year = {2016},
date = {2016-04-29},
urldate = {2016-04-29},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16)},
pages = {29-34},
publisher = { i6doc.com},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_774434,
title = {ESNigma: efficient feature selection for Echo State Networks},
author = {Bacciu Davide and Benedetti Filippo and Micheli Alessio},
url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-104.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15)},
pages = {189--194},
publisher = {i6doc.com publ.},
abstract = {The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as $60%$ of the time required by the original wrapper.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{bacciuJirs15,
title = {Robotic Ubiquitous Cognitive Ecology for Smart Homes},
author = {Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and McGinnity T Martin and Micheli Alessio and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio and Vance Philip},
url = {http://dx.doi.org/10.1007/s10846-015-0178-2},
doi = {10.1007/s10846-015-0178-2},
issn = {0921-0296},
year = {2015},
date = {2015-01-01},
journal = {Journal of Intelligent & Robotic Systems},
volume = {80},
number = {1},
pages = {57-81},
publisher = {Springer Netherlands},
abstract = {Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{icfEann14,
title = {An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications},
author = {Bacciu Davide},
doi = {10.1007/978-3-319-11071-4_4},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Communications in Computer and Information Science - Engineering Applications of Neural Networks},
journal = {COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE},
volume = {459},
pages = {39--48},
publisher = {Springer International Publishing},
abstract = {The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised
to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data
from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data
from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.@conference{11568_586070,
title = {Integrating bi-directional contexts in a generative kernel for trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2014.6889768},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Neural Networks (IJCNN), 2014 International Joint Conference on},
pages = {4145--4151},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{bacciuNeuroComp2013,
title = {An input–output hidden Markov model for tree transductions},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro },
url = {http://www.sciencedirect.com/science/article/pii/S0925231213001914},
doi = {10.1016/j.neucom.2012.12.044},
issn = {0925-2312},
year = {2013},
date = {2013-01-01},
journal = {Neurocomputing},
volume = {112},
pages = {34--46},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_159900,
title = {An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living},
author = {Bacciu Davide and CHESSA Stefano and Gallicchio Claudio and MICHELI Alessio and Barsocchi Paolo},
doi = {10.1007/978-3-642-35467-0_5},
year = {2013},
date = {2013-01-01},
booktitle = {Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets},
journal = {SMART INNOVATION, SYSTEMS AND TECHNOLOGIES},
volume = {19},
pages = {41--50},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{di2013italian,
title = {Italian Machine Learning and Data Mining research: The last years},
author = {Di Mauro Nicola and Frasconi Paolo and Angiulli Fabrizio and Bacciu Davide and de Gemmis Marco and Esposito Floriana and Fanizzi Nicola and Ferilli Stefano and Gori Marco and Lisi Francesca A and others},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263},
doi = {10.3233/IA-130050},
year = {2013},
date = {2013-01-01},
journal = {Intelligenza Artificiale},
volume = {7},
number = {2},
pages = {77--89},
publisher = {IOS Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}