Alesiani, Francesco

16 publications

ICML 2025 Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani

NeurIPSW 2024 Discrete-Continuous Variational Optimization with Local Gradients Jonathan H Warrell, Francesco Alesiani, Cameron Smith, Anja Mösch, Martin Renqiang Min

NeurIPSW 2024 Gradient of Clifford Neural Networks Takashi Maruyama, Francesco Alesiani

ICLRW 2024 Hierarchy-Based Clifford Group Equivariant Message Passing Neural Networks Takashi Maruyama, Francesco Alesiani

NeurIPS 2024 Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert

ICMLW 2023 Differentiable MaxSAT Message Passing Francesco Alesiani, Cristóbal Corvalán Morbiducci, Markus Zopf

AAAI 2023 Implicit Bilevel Optimization: Differentiating Through Bilevel Optimization Programming Francesco Alesiani

ICML 2023 Learning Neural PDE Solvers with Parameter-Guided Channel Attention Makoto Takamoto, Francesco Alesiani, Mathias Niepert

ICLRW 2023 Pdebench: An Extensive Benchmark for Sci- Entific Machine Learning Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

NeurIPS 2022 PDEBench: An Extensive Benchmark for Scientific Machine Learning Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

UAI 2022 Principle of Relevant Information for Graph Sparsification Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C. Principe

AAAI 2021 Measuring Dependence with Matrix-Based Entropy Functional Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, José C. Príncipe

IJCAI 2021 Reinforcement Learning for Route Optimization with Robustness Guarantees Tobias Jacobs, Francesco Alesiani, Gülcin Ermis

IJCAI 2020 Measuring the Discrepancy Between Conditional Distributions: Methods, Properties and Applications Shujian Yu, Ammar Shaker, Francesco Alesiani, José C. Príncipe

ECML-PKDD 2020 Towards Interpretable Multi-Task Learning Using Bilevel Programming Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin

AAAI 2019 Efficient and Scalable Multi-Task Regression on Massive Number of Tasks Xiao He, Francesco Alesiani, Ammar Shaker