Per Year
@article{skeislr-csur56, acm = {3645103}, articleno = 161, author = {Ciatto, Giovanni and Sabbatini, Federico and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea}, dblp = {journals/csur/CiattoSAMO24}, doi = {10.1145/3645103}, eissn = {1557-734}, iris = {11585/969235}, issn = {0360-0300}, journal = {ACM Computing Surveys}, keywords = {Logic; Machine learning theory; Hybrid symbolic-numeric methods; Knowledge representation and reasoning}, month = jun, number = 6, numpages = 35, pages = {161:1--161:35}, publisher = {ACM}, scholar = {13701373869146776438}, scopus = {2-s2.0-85188835517}, semanticscholar = {267611660}, title = {Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review}, url = {https://dl.acm.org/doi/10.1145/3645103}, urlopenaccess = {https://dl.acm.org/doi/pdf/10.1145/3645103}, urlpdf = {https://dl.acm.org/doi/pdf/10.1145/3645103}, volume = 56, wos = {WOS:001208566200027}, year = 2024 }
@article{skenlp-jaamas38, articleno = 32, author = {Agiollo, Andrea and Siebert, Luciano Cavalcante and Murukannaiah, Pradeep Kumar and Omicini, Andrea}, dblp = {journals/aamas/AgiolloSMO24}, doi = {10.1007/s10458-024-09663-8}, iris = {11585/973894}, journal = {Autonomous Agents and Multi-Agent Systems}, keywords = {Natural language processing, post-hoc explanations, symbolic knowledge extraction, eXplainable AI, resource-friendly AI}, month = jul, note = {Special Issue on Multi-Agent Systems and Explainable AI}, numpages = 33, pages = {1--33}, publisher = {Springer}, scholar = {13114388339509722903}, scopus = {2-s2.0-85197706028}, semanticscholar = {271083044}, title = {From Large Language Models to Small Logic Programs: Building Global Explanations from Disagreeing Local Post-hoc Explainers}, url = {http://link.springer.com/10.1007/s10458-024-09663-8}, urlopenaccess = {https://link.springer.com/content/pdf/10.1007/s10458-024-09663-8.pdf}, urlpdf = {https://link.springer.com/content/pdf/10.1007/s10458-024-09663-8.pdf}, volume = 38, wos = {WOS:001264787900001}, year = 2024 }
@article{eneafl-fgcs154, author = {Agiollo, Andrea and Bellavista, Paolo and Mendula, Matteo and Omicini, Andrea}, dblp = {journals/fgcs/AgiolloBMO24}, doi = {10.1016/j.future.2024.01.007}, editor = {Hao Wu and Carlo Puliafito and Omer F. Rana and Luiz F. Bittencourt}, iris = {11585/953081}, issn = {0167-739X}, journal = {Future Generation Computer Systems}, keywords = {Serverless, Federated Learning, Energy Management, Internet of Things, Resource-constrained Learning}, month = may, note = {Special Issue ``Serverless Computing in the Cloud-to-Edge Continuum''}, numpages = 16, pages = {219--234}, publisher = {Elsevier Science B.V.}, scholar = {15164122000920541506}, scopus = {2-s2.0-85182399653}, semanticscholar = {268131278}, title = {{EneA-FL}: Energy-aware Orchestration for Serverless Federated Learning}, url = {https://www.sciencedirect.com/science/article/pii/S0167739X24000074}, volume = 154, wos = {WOS:001164533000001}, year = 2024 }
@inproceedings{skidatadegradation-woa2024, author = {Rafanelli, Andrea and Magnini, Matteo and Agiollo, Andrea and Ciatto, Giovanni and Omicini, Andrea}, booktitle = {WOA 2024 -- 25th Workshop ``From Objects to Agents 2024''}, dblp = {conf/woa/RafanelliMACO24}, editor = {Alderighi, Marco and Baldoni, Matteo and Baroglio, Cristina and Micalizio, Roberto and Tedeschi, Stefano}, iris = {11585/975934}, issn = {1613-0073}, keywords = {Symbolic Knowledge Injection, Robustness, Neural Networks}, location = {Bard, AO, Italy}, month = jul, numpages = 13, pages = {20--32}, publisher = {Sun SITE Central Europe, RWTH Aachen University}, scholar = {9917174396528512749}, scopus = {2-s2.0-85200118300}, series = {CEUR Workshop Proceedings}, subseries = {AIxIA Series}, title = {An Empirical Study on the Robustness of Knowledge Injection Techniques Against Data Degradation}, url = {https://ceur-ws.org/Vol-3735/paper_02.pdf}, urlopenaccess = {https://ceur-ws.org/Vol-3735/paper_02.pdf}, urlpdf = {https://ceur-ws.org/Vol-3735/paper_02.pdf}, volume = 3735, 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 }