Publications

2023

1. arXiv

   Graph Deep Learning for Time Series Forecasting

   A. Cini, I. Marisca, D. Zambon, and C. Alippi

   arXiv preprint arXiv:2305.19183 2023

   Bib

   @article{cini2023graphdeep,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Graph Deep Learning for Time Series Forecasting},
     author = {Cini, A. and Marisca, I. and Zambon, D. and Alippi, C.},
     journal = {arXiv preprint arXiv:2305.19183},
     year = {2023}
   }

2. arXiv

   Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

   A. Cini, D. Mandic, and C. Alippi

   arXiv preprint arXiv:2305.19183 2023

   Bib

   @article{cini2023graph,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting},
     author = {Cini, A. and Mandic, D. and Alippi, C.},
     journal = {arXiv preprint arXiv:2305.19183},
     year = {2023}
   }

3. NeurIPS

   Taming Local Effects in Graph-based Spatiotemporal Forecasting

   A. Cini*, I. Marisca*, D. Zambon, and C. Alippi

   In Advances in Neural Information Processing Systems 2023

   Bib

   @inproceedings{cini2023taming,
     abbr = {NeurIPS},
     bibtex_show = {true},
     title = {Taming Local Effects in Graph-based Spatiotemporal Forecasting},
     author = {Cini*, A. and Marisca*, I. and Zambon, D. and Alippi, C.},
     booktitle = {Advances in Neural Information Processing Systems},
     year = {2023}
   }

4. AAAI

   Scalable Spatiotemporal Graph Neural Networks

   A. Cini*, I. Marisca*, F.M. Bianchi, and C. Alippi

   In Proceedings of the AAAI Conference on Artificial Intelligence 2023

   Bib

   @inproceedings{cini2023scalable,
     abbr = {AAAI},
     bibtex_show = {true},
     title = {Scalable Spatiotemporal Graph Neural Networks},
     author = {Cini*, A. and Marisca*, I. and Bianchi, F.M. and Alippi, C.},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     year = {2023}
   }

5. SEGAN

   Peak shaving in distribution networks using stationary energy storage systems: A Swiss case study

   N. A Efkarpidis, S. Imoscopi, M. Geidl,  A. Cini, S. Lukovic, C. Alippi, and I. Herbst

   Sustainable Energy, Grids and Networks 2023

   Bib

   @article{efkarpidis2023peak,
     abbr = {SEGAN},
     bibtex_show = {true},
     title = {Peak shaving in distribution networks using stationary energy storage systems: A Swiss case study},
     author = {Efkarpidis, N. A and Imoscopi, S. and Geidl, M. and Cini, A. and Lukovic, S. and Alippi, C. and Herbst, I.},
     journal = {Sustainable Energy, Grids and Networks},
     volume = {34},
     pages = {101018},
     year = {2023},
     publisher = {Elsevier}
   }

6. JMLR

   Sparse Graph Learning from Spatiotemporal Time Series

   A. Cini, D. Zambon, and C. Alippi

   Journal of Machine Learning Research 2023

   Bib

   @article{cini2022sparse,
     abbr = {JMLR},
     bibtex_show = {true},
     title = {Sparse Graph Learning from Spatiotemporal Time Series},
     author = {Cini, A. and Zambon, D. and Alippi, C.},
     journal = {Journal of Machine Learning Research},
     volume = {24},
     number = {242},
     pages = {1--36},
     year = {2023}
   }

7. arXiv

   Graph state-space models

   D. Zambon,  A. Cini, L. Livi, and C. Alippi

   In arXiv preprint arXiv:2301.01741 2023

   Bib

   @inproceedings{zambon2023graph,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Graph state-space models},
     author = {Zambon, D. and Cini, A. and Livi, L. and Alippi, C.},
     booktitle = {arXiv preprint arXiv:2301.01741},
     year = {2023}
   }

2022

1. NeurIPS

   Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

   I. Marisca*,  A. Cini*, and C. Alippi

   In Advances in Neural Information Processing Systems 2022

   Bib

   @inproceedings{marisca2022learning,
     abbr = {NeurIPS},
     bibtex_show = {true},
     title = {Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations},
     author = {Marisca*, I. and Cini*, A. and Alippi, C.},
     booktitle = {Advances in Neural Information Processing Systems},
     year = {2022}
   }

2. RLDM

   Deep Reinforcement Learning with Weighted Q-Learning

   A. Cini, C. D’Eramo, J. Peters, and C. Alippi

   The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2022

   Bib

   @article{cini2020deep,
     abbr = {RLDM},
     bibtex_show = {true},
     title = {Deep Reinforcement Learning with Weighted Q-Learning},
     author = {Cini, A. and D'Eramo, C. and Peters, J. and Alippi, C.},
     journal = {The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)},
     year = {2022}
   }

3. ICLR

   Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

   A. Cini*, I. Marisca*, and C. Alippi

   In International Conference on Learning Representations 2022

   Bib

   @inproceedings{cini2022filling,
     abbr = {ICLR},
     bibtex_show = {true},
     title = {Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks},
     author = {Cini*, A. and Marisca*, I. and Alippi, C.},
     booktitle = {International Conference on Learning Representations},
     year = {2022}
   }

4. TODAES

   Graph Neural Networks for High-Level Synthesis Design Space Exploration

   L. Ferretti*,  A. Cini*, G. Zacharopoulos, C. Alippi, and L. Pozzi

   ACM Trans. Des. Autom. Electron. Syst. 2022

   Bib

   @article{ferretti2021graph,
     abbr = {TODAES},
     bibtex_show = {true},
     title = {Graph Neural Networks for High-Level Synthesis Design Space Exploration},
     author = {Ferretti*, L. and Cini*, A. and Zacharopoulos, G. and Alippi, C. and Pozzi, L.},
     journal = {ACM Trans. Des. Autom. Electron. Syst.},
     year = {2022}
   }

2021

1. JMLR

   Gaussian Approximation for Bias Reduction in Q-Learning

   C. D’Eramo*,  A. Cini*, A. Nuara, M. Pirotta, C. Alippi, J. Peters, and M. Restelli

   Journal of Machine Learning Research 2021

   Bib

   @article{deramo2021gaussian,
     abbr = {JMLR},
     bibtex_show = {true},
     title = {Gaussian Approximation for Bias Reduction in Q-Learning},
     author = {D'Eramo*, C. and Cini*, A. and Nuara, A. and Pirotta, M. and Alippi, C. and Peters, J. and Restelli, M.},
     journal = {Journal of Machine Learning Research},
     year = {2021},
     volume = {22},
     number = {277},
     pages = {1-51},
     url = {http://jmlr.org/papers/v22/20-633.html}
   }

2020

1. IJCNN

   Cluster-based Aggregate Load Forecasting with Deep Neural Networks

   A. Cini, S. Lukovic, and C. Alippi

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

   Bib

   @inproceedings{cini2020cluster,
     bibtex_show = {true},
     abbr = {IJCNN},
     title = {Cluster-based Aggregate Load Forecasting with Deep Neural Networks},
     author = {Cini, A. and Lukovic, S. and Alippi, C.},
     booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},
     pages = {1--8},
     year = {2020},
     organization = {IEEE}
   }

2019

1. IJCNN

   Exploiting Action-Value uncertainty to drive exploration in reinforcement learning

   C. D’Eramo,  A. Cini, and M. Restelli

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

   Bib

   @inproceedings{d2019exploiting,
     abbr = {IJCNN},
     bibtex_show = {true},
     title = {Exploiting Action-Value uncertainty to drive exploration in reinforcement learning},
     author = {D’Eramo, C. and Cini, A. and Restelli, M.},
     booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},
     pages = {1--8},
     year = {2019},
     organization = {IEEE}
   }