Federico Errica

49 papers A* 9B 8Journal 29Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Not All Layers Are Created Equal: Adaptive LoRA Ranks for Personalized Image Generation. CoRR Donald Shenaj, Federico Errica, Antonio Carta
2025	A*	`conf`	Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. ICML Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani
2025	J	`jnl`	Adaptive Width Neural Networks. CoRR Federico Errica, Henrik Christiansen, Viktor Zaverkin, Mathias Niepert, Francesco Alesiani
2025	J	`jnl`	Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. CoRR Henrik Christiansen, Takashi Maruyama, Federico Errica, Viktor Zaverkin, Makoto Takamoto, Francesco Alesiani
2025	J	`jnl`	Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies. CoRR Joël Mathys, Federico Errica
2025	J	`jnl`	Oversmoothing, "Oversquashing", Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning. CoRR Adrián Arnaiz-Rodríguez, Federico Errica
2025	J	`jnl`	Variational Kolmogorov-Arnold Network. CoRR Francesco Alesiani, Henrik Christiansen, Federico Errica
2025	—	`conf`	What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering. NAACL (Long Papers) Federico Errica, Davide Sanvito, Giuseppe Siracusano, Roberto Bifulco
2024	J	`jnl`	Deep Graph Networks for Drug Repurposing With Multi-Protein Targets. IEEE Trans. Emerg. Top. Comput. Davide Bacciu, Federico Errica, Alessio Gravina, Lorenzo Madeddu, Marco Podda, Giovanni Stilo
2024	A*	`conf`	Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. NeurIPS Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert
2024	J	`jnl`	Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. CoRR Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert
2024	A*	`conf`	History Repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. IJCAI Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schülke, Heiner Stuckenschmidt
2024	J	`jnl`	History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting. CoRR Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt
2024	A*	`conf`	Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. ICLR Federico Errica, Mathias Niepert
2024	J	`jnl`	What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering. CoRR Federico Errica, Giuseppe Siracusano, Davide Sanvito, Roberto Bifulco
2023	J	`jnl`	Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. CoRR Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani
2023	B	`conf`	Graph Representation Learning. ESANN Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon
2023	B	`conf`	Hidden Markov Models for Temporal Graph Representation Learning. ESANN Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli
2023	J	`jnl`	Investigating the Interplay between Features and Structures in Graph Learning. CoRR Daniele Castellana, Federico Errica
2023	J	`jnl`	Modeling Edge Features with Deep Bayesian Graph Networks. CoRR Daniele Atzeni, Federico Errica, Davide Bacciu, Alessio Micheli
2023	A*	`conf`	On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data. NeurIPS Federico Errica
2023	J	`jnl`	PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs. J. Open Source Softw. Federico Errica, Davide Bacciu, Alessio Micheli
2023	J	`jnl`	Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. CoRR Henrik Christiansen, Federico Errica, Francesco Alesiani
2023	J	`jnl`	Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. CoRR Federico Errica, Mathias Niepert
2022	J	`jnl`	Bayesian Deep Learning for Graphs. CoRR Federico Errica
2022	—	`—`	Bayesian Deep Learning for Graphs. Federico Errica
2022	J	`jnl`	Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. Frontiers Artif. Intell. Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
2022	B	`conf`	Deep Learning for Graphs. ESANN Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon
2022	A*	`conf`	The Infinite Contextual Graph Markov Model. ICML Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli
2022	J	`jnl`	Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti
2021	J	`jnl`	Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. CoRR Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
2021	B	`conf`	Complex Data: Learning Trustworthily, Automatically, and with Guarantees. ESANN Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti
2021	B	`conf`	Concept Matching for Low-Resource Classification. IJCNN Federico Errica, Fabrizio Silvestri, Bora Edizel, Ludovic Denoyer, Fabio Petroni, Vassilis Plachouras, Sebastian Riedel
2021	A*	`conf`	Graph Mixture Density Networks. ICML Federico Errica, Davide Bacciu, Alessio Micheli
2021	B	`conf`	Modeling Edge Features with Deep Bayesian Graph Networks. IJCNN Daniele Atzeni, Davide Bacciu, Federico Errica, Alessio Micheli
2021	B	`conf`	Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. ESANN Federico Errica, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Alessio Micheli
2020	A*	`conf`	A Fair Comparison of Graph Neural Networks for Graph Classification. ICLR Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli
2020	J	`jnl`	A gentle introduction to deep learning for graphs. Neural Networks Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda
2020	J	`jnl`	Accelerating the identification of informative reduced representations of proteins with deep learning for graphs. CoRR Federico Errica, Marco Giulini, Davide Bacciu, Roberto Menichetti, Alessio Micheli, Raffaello Potestio
2020	J	`jnl`	Concept Matching for Low-Resource Classification. CoRR Federico Errica, Ludovic Denoyer, Bora Edizel, Fabio Petroni, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel
2020	J	`jnl`	Graph Mixture Density Networks. CoRR Federico Errica, Davide Bacciu, Alessio Micheli
2020	J	`jnl`	Probabilistic Learning on Graphs via Contextual Architectures. J. Mach. Learn. Res. Davide Bacciu, Federico Errica, Alessio Micheli
2020	B	`conf`	Theoretically Expressive and Edge-aware Graph Learning. ESANN Federico Errica, Davide Bacciu, Alessio Micheli
2020	J	`jnl`	Theoretically Expressive and Edge-aware Graph Learning. CoRR Federico Errica, Davide Bacciu, Alessio Micheli
2019	J	`jnl`	A Fair Comparison of Graph Neural Networks for Graph Classification. CoRR Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli
2019	J	`jnl`	A Gentle Introduction to Deep Learning for Graphs. CoRR Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda
2018	A*	`conf`	Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. ICML Davide Bacciu, Federico Errica, Alessio Micheli
2018	J	`jnl`	Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. CoRR Davide Bacciu, Federico Errica, Alessio Micheli
2017	—	`conf`	FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. SemEval@ACL Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto