A comparative study of machine learning algorithms for physiological signal classification. - Giorgio Biagetti; Paolo Crippa; Laura Falaschetti; Giulia Tanoni; Claudio Turchetti (KES, 2018)

Weakly Supervised Transfer Learning for Multi-label Appliance Classification. - Giulia Tanoni; Emanuele Principi; Luigi Mandolini; Stefano Squartini (AII, 2022)

A Multiple Instance Regression Approach to Electrical Load Disaggregation. - Luca Serafini; Giulia Tanoni; Emanuele Principi; Susanna Spinsante; Stefano Squartini (EUSIPCO, 2022)

Improving Knowledge Distillation for Non-Intrusive Load Monitoring Through Explainability Guided Learning. - Djordje Batic; Giulia Tanoni; Lina Stankovic; Vladimir Stankovic 001; Emanuele Principi (ICASSP, 2023)

Appliance-Incremental Learning for Non-Intrusive Load Monitoring. - Giulia Tanoni; Emanuele Principi; Luigi Mandolini; Stefano Squartini (SmartGridComm, 2023)

Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring. - Giulia Tanoni; Emanuele Principi; Stefano Squartini (IEEE Trans. Smart Grid, 2023)

Knowledge Distillation for Scalable Nonintrusive Load Monitoring. - Giulia Tanoni; Lina Stankovic; Vladimir Stankovic 001; Stefano Squartini; Emanuele Principi (IEEE Trans. Ind. Informatics, 2024)

A Deep Cascade Framework for Non-Intrusive Power Disaggregation in Solar-Powered Households. - Giulia Tanoni; Redemptor Laceda Taloma; Emanuele Principi; Danilo Comminiello; Stefano Squartini (ISCAS, 2025)

Interpretability and reliability-driven knowledge distillation for non-intrusive load monitoring on the edge. - Djordje Batic; Giulia Tanoni; Emanuele Principi; Lina Stankovic; Vladimir Stankovic 001; Stefano Squartini (Expert Syst. Appl., 2025)

A weakly supervised active learning framework for non-intrusive load monitoring. - Giulia Tanoni; Tamara Sobot; Emanuele Principi; Vladimir Stankovic 001; Lina Stankovic; Stefano Squartini (Integr. Comput. Aided Eng., 2025)