Per Year

@inproceedings{llmbasedhealthcarechatbots-telmed2024,
 author={Montagna, Sara and Aguzzi, Gianluca and Ferretti, Stefano and Pengo, Martino Francesco and Klopfenstein, Lorenz Cuno and Ungolo, Michelangelo and Magnini, Matteo},
 booktitle={2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
keywords = {large language model, medical chatbot, chronic disease management},
 title={LLM-based Solutions for Healthcare Chatbots: a Comparative Analysis},
 year={2024},
 volume={},
 number={},
 pages={346-351},
 keywords={Pervasive computing;Privacy;Filtering;Conferences;Computational modeling;Medical services;Chatbots;Large Language Model;Medical Chatbot;Chronic Disease Management},
 doi={10.1109/PerComWorkshops59983.2024.10503257}}

@article{ndnfl-fgcs2023,
   author = {Agiollo, Andrea and Enkeleda Bardhi and Mauro Conti and Nicolò Dal Fabbro and Riccardo Lazzeretti},
   doi = {10.1016/j.future.2023.11.009},
   issn = {0167-739X},
   journal = {Future Generation Computer Systems},
   keywords = {Anonymous communication, Federated Learning, Named Data Networking, Privacy-preserving},
   pages = {288--303},
   publisher = {Elsevier},
   note = {Special Issue on Federated Learning on the Edge: Challenges and Future Directions},
   title = {Anonymous Federated Learning via Named-Data Networking},
   url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004144},
   volume = 152,
   year = 2024
}

@inproceedings{agentslargemodels-aamas2024,
   address = {Auckland, New Zealand},
   author = {Ricci, Alessandro and Mariani, Stefano and Zambonelli, Franco and Burattini, Samuele and Castelfranchi, Cristiano},
   booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)},
   editor = {Alechina, Natasha and Dignum, Virginia and Dastani, Mehdi and Sichman, Jaime Sim{\~a}o},
   isbn = {978-1-4007-0486-4},
   month = {6--10~} # may,
   pages = {2706--2711},
   publisher = {IFAAMAS},
   title = {The Cognitive Hourglass: Agent Abstractions in the Large Models Era},
   url = {https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p2706.pdf},
   urlopenaccess = {https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p2706.pdf},
   urlpdf = {https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p2706.pdf},
   year = 2024
}

@inproceedings{samis-kdd2024,
   acm = {3671985},
   author = {Agiollo, Andrea and Young In Kim and Khanna, Rajiv},
   dblp = {conf/kdd/AgiolloKK24},
   doi = {10.1145/3637528.3671985},
   journal = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), August 25–29, 2024, Barcelona, Spain},
   keywords = {Neural Networks, Data-efficient Learning, Memorization, Flatness},
   month = aug,
   numpages = 12,
   pages = {17--28},
   publisher = {ACM},
   scholar = {8151925308990231563},
   title = {Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization},
   url = {https://dl.acm.org/doi/10.1145/3637528.3671985},
   urlopenaccess = {https://dl.acm.org/doi/pdf/10.1145/3637528.3671985},
   urlpdf = {https://dl.acm.org/doi/pdf/10.1145/3637528.3671985},
   year = 2024
}