Sustainable AI, Hybrid Systems, Computational Learning Theory, Applications for Critical Infrastructure
Incremental learning and learning with drift, learning from limited data set, learning with label noise or few labels, reliability of learning, efficient deep learning, fairness of ML; explainable ML, readability of learning, learning with structured data, prototype-based models, graph neural networks, recurrent and recursive models; biomedical applications
Published at: Machine Learning (Volume: 112)
DOI: 10.1007/s10994-023-06385-y
Published at: Neurocomputing (Volume: 558)
DOI: 10.1016/j.neucom.2023.126722
Published at: Neurocomputing (Volume: 555)
DOI: 10.1016/j.neucom.2023.126640
Published at: Annual Review of Resource Economics (Volume: 15)
DOI: 10.1146/annurev-resource-101722-082743
Published at: Neural Processing Letters (Volume: 55)
DOI: 10.1007/s11063-022-10826-5
Published at: SN Computer Science (Volume: 4)
DOI: 10.1007/s42979-023-01782-5
Published at: Algorithms (Volume: 16)
DOI: 10.3390/a16040205
Published at: Neural Computing and Applications (Volume: 35)
DOI: 10.1007/s00521-022-08115-2
Published at: Communications in Computer and Information Science (Volume: 1901 CCIS)
DOI: 10.1007/978-3-031-44064-9_11
Published at: Communications in Computer and Information Science (Volume: 1903 CCIS)
DOI: 10.1007/978-3-031-44070-0_14