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}}},
    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
}