Federico Errica

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 38

Top Topics

Top Venues

Publications

Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. CoRR (2024)
Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. CoRR (2024)
Federico Errica, Giuseppe Siracusano, Davide Sanvito, Roberto Bifulco
What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering. CoRR (2024)
Federico Errica, Mathias Niepert
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. ICLR (2024)
Davide Bacciu, Federico Errica, Alessio Gravina, Lorenzo Madeddu, Marco Podda, Giovanni Stilo
Deep Graph Networks for Drug Repurposing With Multi-Protein Targets. IEEE Trans. Emerg. Top. Comput. 12 (1) (2024)
Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. CoRR (2023)
Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli
Hidden Markov Models for Temporal Graph Representation Learning. ESANN (2023)
Daniele Castellana, Federico Errica
Investigating the Interplay between Features and Structures in Graph Learning. CoRR (2023)
Federico Errica
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data. NeurIPS (2023)
Federico Errica, Davide Bacciu, Alessio Micheli
PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. J. Open Source Softw. 8 (90) (2023)
Federico Errica, Mathias Niepert
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. CoRR (2023)
Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon
Graph Representation Learning. ESANN (2023)
Daniele Atzeni, Federico Errica, Davide Bacciu, Alessio Micheli
Modeling Edge Features with Deep Bayesian Graph Networks. CoRR (2023)
Henrik Christiansen, Federico Errica, Francesco Alesiani
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. CoRR (2023)
Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon
Deep Learning for Graphs. ESANN (2022)
Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. Frontiers Artif. Intell. 5 (2022)
Federico Errica
Bayesian Deep Learning for Graphs. CoRR (2022)
Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing 493 (2022)
Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli
The Infinite Contextual Graph Markov Model. ICML (2022)
Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti
Complex Data: Learning Trustworthily, Automatically, and with Guarantees. ESANN (2021)
Federico Errica, Fabrizio Silvestri, Bora Edizel, Ludovic Denoyer, Fabio Petroni, Vassilis Plachouras, Sebastian Riedel
Concept Matching for Low-Resource Classification. IJCNN (2021)
Federico Errica, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Alessio Micheli
Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. ESANN (2021)
Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. CoRR (2021)
Federico Errica, Davide Bacciu, Alessio Micheli
Graph Mixture Density Networks. ICML (2021)
Daniele Atzeni, Davide Bacciu, Federico Errica, Alessio Micheli
Modeling Edge Features with Deep Bayesian Graph Networks. IJCNN (2021)
Davide Bacciu, Federico Errica, Alessio Micheli
Probabilistic Learning on Graphs via Contextual Architectures. J. Mach. Learn. Res. 21 (2020)
Federico Errica, Davide Bacciu, Alessio Micheli
Theoretically Expressive and Edge-aware Graph Learning. ESANN (2020)
Federico Errica, Davide Bacciu, Alessio Micheli
Theoretically Expressive and Edge-aware Graph Learning. CoRR (2020)
Federico Errica, Davide Bacciu, Alessio Micheli
Graph Mixture Density Networks. CoRR (2020)
Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli
A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR (2020)
Federico Errica, Marco Giulini, Davide Bacciu, Roberto Menichetti, Alessio Micheli, Raffaello Potestio
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs. CoRR (2020)
Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda
A gentle introduction to deep learning for graphs. Neural Networks 129 (2020)
Federico Errica, Ludovic Denoyer, Bora Edizel, Fabio Petroni, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel
Concept Matching for Low-Resource Classification. CoRR (2020)
Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda
A Gentle Introduction to Deep Learning for Graphs. CoRR (2019)
Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli
A Fair Comparison of Graph Neural Networks for Graph Classification. CoRR (2019)
Davide Bacciu, Federico Errica, Alessio Micheli
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. ICML (2018)
Davide Bacciu, Federico Errica, Alessio Micheli
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. CoRR (2018)
Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. SemEval@ACL (2017)