Publications

2025

1. Journal

   BN-Pool: a Bayesian Nonparametric Approach to Graph Pooling

   Daniele Castellana, and Filippo Maria Bianchi

   Journal of Machine Learning Research, Oct 2025

   Under review
2. Journal

   CD-IMM: A Bayesian Non-parametric Classifier for Continual Learning with Class Repetitions

   Daniele Castellana, and Antonio Carta

   Neural Networks, Oct 2025

   Under review
3. Journal

   Bayesian Non-Parametric Anomaly Detection for Autonomous Spacecraft

   Daniele Castellana, Geremia Pompei, Lorenzo Allegrini, and 5 more authors

   Engineering Applications of Artificial Intelligence, Oct 2025

   Under review
4. Journal

   Predictive modeling of biogeographical ancestry using a novel SNP panel and supervised learning approaches

   Cosimo Grazzini, Giorgia Spera, Stefania Morelli, and 5 more authors

   Expert Systems with Applications, Sep 2025

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5. Workshop

   Modelling Dynamic Networks via Neural Graph-ODE: an Application to the Trade Network

   Daniele Castellana, Luisa Collodi, and Michele Boreale

   In Third DISEI-Workshop on Heterogeneity, Evolution and Networks in Economics, Sep 2025

   Accepted as Extended Abstract
6. Workshop

   BN-Pool: a Bayesian Nonparametric Approach to Graph Pooling

   Daniele Castellana, and Filippo Maria Bianchi

   In 1st ComBayNS Workshop, IJCNN 2025, Jul 2025
7. Conference

   Generate Polyphonic Music with Multivariate Masked Autoregressive Flow

   Massimiliano Sirgiovanni, and Daniele Castellana

   In 2025 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2025
8. Conference

   Biogeographical Ancestry Prediction via an Innovative Panel: Difficult Task or Complex Phenomenon?

   Cosimo Grazzini, Giorgia Spera, Daniele Castellana, and 4 more authors

   In Statistics for Innovation III, Apr 2025

2024

1. Conference

   CD-IMM: The Benefits of Domain-based Mixture Models in Bayesian Continual Learning

   Daniele Castellana, Antonio Carta, and Davide Bacciu

   In Proceedings of the 1st ContinualAI Unconference, 2023, Oct 2024

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   Real-world streams of data are characterised by the continuous occurrence of new and old classes, possibly on novel domains. Bayesian non-parametric mixture models provide a natural solution for continual learning due to their ability to create new components on the fly when new data are observed. However, popular class-based and time-based mixtures are often tested on simplified streams (eg class-incremental), where shortcuts can be exploited to infer drifts. We hypothesise that domain-based mixtures are more effective on natural streams. Our proposed method, the CD-IMM, exemplifies this approach by learning an infinite mixture of domains for each class. We experiment on a natural scenario with a mix of class repetitions and novel domains to validate our hypothesis. The experimental results confirm our hypothesis and we find that CD-IMM beats state-of-the-art bayesian continual learning methods.

2. Conference

   Lying Graph Convolution: Learning to Lie for Node Classification Tasks

   Daniele Castellana

   In 2024 International Joint Conference on Neural Networks (IJCNN), Oct 2024

2023

1. Workshop

   Investigating the Interplay between Features and Structures in Graph Learning

   Daniele Castellana, and Federico Errica

   In 20th International Workshop on Mining and Learning with Graphs, Sep 2023

2022

1. Conference

   The Infinite Contextual Graph Markov Model

   Daniele Castellana, Federico Errica, Davide Bacciu, and 1 more author

   In Proceedings of the 39th International Conference on Machine Learning, Jul 2022

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   The Contextual Graph Markov Model (CGMM) is a deep, unsupervised, and probabilistic model for graphs that is trained incrementally on a layer-by-layer basis. As with most Deep Graph Networks, an inherent limitation is the need to perform an extensive model selection to choose the proper size of each layer’s latent representation. In this paper, we address this problem by introducing the Infinite Contextual Graph Markov Model (iCGMM), the first deep Bayesian nonparametric model for graph learning. During training, iCGMM can adapt the complexity of each layer to better fit the underlying data distribution. On 8 graph classification tasks, we show that iCGMM: i) successfully recovers or improves CGMM’s performances while reducing the hyper-parameters’ search space; ii) performs comparably to most end-to-end supervised methods. The results include studies on the importance of depth, hyper-parameters, and compression of the graph embeddings. We also introduce a novel approximated inference procedure that better deals with larger graph topologies.

2021

1. Thesis

   A tensor framework for learning in structured domains

   Daniele Castellana

   Department of Computer Science, Università di Pisa, May 2021
2. Journal

   A tensor framework for learning in structured domains

   Daniele Castellana, and Davide Bacciu

   Neurocomputing, May 2021

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   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.

2020

1. Conference

   Learning from Non-Binary Constituency Trees via Tensor Decomposition

   Daniele Castellana, and Davide Bacciu

   In Proceedings of the 28th International Conference on Computational Linguistics, Dec 2020

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   Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation procedure changes deeply the structure, furthering constituents that instead are close. In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models. In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure. A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters. Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.

2. Conference

   Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data

   Daniele Castellana, and Davide Bacciu

   In Proceedings of the the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Oct 2020

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   The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data. They leverage the Canonical and Tensor-Train decompositions to yield expressive context aggregation while limiting the number of model parameters. Finally, we define two novel neural recursive models for trees leveraging such aggrega-tion functions, and we test them on two tree classification tasks, showing the advantage of proposed models when tree outdegree increases.

3. Conference

   Generalising Recursive Neural Models by Tensor Decomposition

   Daniele Castellana, and Davide Bacciu

   In 2020 International Joint Conference on Neural Networks (IJCNN), Jul 2020

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   Most machine learning models for structured data encode the structural knowledge of a node by leveraging simple aggregation functions (in neural models, typically a weighted sum) of the information in the node’s neighbourhood. Nevertheless, the choice of simple context aggregation functions, such as the sum, can be widely sub-optimal. In this work we introduce a general approach to model aggregation of structural context leveraging a tensor-based formulation. We show how the exponential growth in the size of the parameter space can be controlled through an approximation based on the Tucker tensor decomposition. This approximation allows limiting the parameters space size, decoupling it from its strict relation with the size of the hidden encoding space. By this means, we can effectively regulate the trade-off between expressivity of the encoding, controlled by the hidden size, computational complexity and model generalisation, influenced by parameterisation. Finally, we introduce a new Tensorial Tree-LSTM derived as an instance of our framework and we use it to experimentally assess our working hypotheses on tree classification scenarios.

2019

1. Conference

   Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models

   Daniele Castellana, and Davide Bacciu

   In 2019 International Joint Conference on Neural Networks (IJCNN), Jul 2019

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   \textcopyright 2019 IEEE. 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.

2. Journal

   Bayesian mixtures of Hidden Tree Markov Models for structured data clustering

   Davide Bacciu, and Daniele Castellana

   Neurocomputing, Jul 2019

   Advances in artificial neural networks, machine learning and computational intelligence

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   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.

2018

1. Workshop

   Learning Tree Distributions by Hidden Markov Models

   Davide Bacciu, and Daniele Castellana

   In Workshop on Learning and Automata (LearnAut’18), Jul 2018
2. Conference

   Mixture of Hidden Markov Models as tree encoder

   Davide Bacciu, and Daniele Castellana

   In Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2018

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   \textcopyright ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 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.