6 papers
Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions
Martino Ciaperoni, Margherita Lalli, Simone Piaggesi +6
Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predic…
Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
Simone Piaggesi, Riccardo Guidotti, Fosca Giannotti +1
Post-hoc explainability is essential for understanding black-box machine learning models. Surrogate-based techniques are widely used for local and global model-agnostic explanation…
Disentangled and Self-Explainable Node Representation Learning
Simone Piaggesi, André Panisson, Megha Khosla
Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised…
DINE: Dimensional Interpretability of Node Embeddings
Simone Piaggesi, Megha Khosla, André Panisson +1
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as…
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
Simone Piaggesi, André Panisson
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. S…
Maximum entropy approaches for the study of triadic motifs in the Mergers & Acquisitions network
Ihusan Adam, Stefano Garlaschi, Jian-Hong Lin +4
In the past years statistical physics has been successfully applied for complex networks modelling. In particular, it has been shown that the maximum entropy principle can be explo…