Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence
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N. Amoroso | A. Monaco | R. Bellotti | Vito Peragine | L. Bellantuono | Flaviana Palmisano | F. Palmisano | V. Peragine
[1] N. Amoroso,et al. Worldwide impact of lifestyle predictors of dementia prevalence: An eXplainable Artificial Intelligence analysis , 2022, Frontiers in Big Data.
[2] N. Amoroso,et al. The spatial association between environmental pollution and long-term cancer mortality in Italy. , 2022, The Science of the total environment.
[3] N. H. Rod,et al. Housing environment and mental health of Europeans during the COVID-19 pandemic: a cross-country comparison , 2022, Scientific Reports.
[4] M. Bardoscia,et al. Territorial bias in university rankings: a complex network approach , 2022, Scientific Reports.
[5] E. Bárcena-Martín,et al. COVID-19 lockdown and housing deprivation across European countries , 2022, Social Science & Medicine.
[6] Agnese Sacchi,et al. Informal we stand? The role of social progress around the world , 2021, International Review of Economics & Finance.
[7] N. Amoroso,et al. Characterization of real-world networks through quantum potentials , 2021, PloS one.
[8] J. Tavares,et al. Explainable Deep Learning for Personalized Age Prediction With Brain Morphology , 2021, Frontiers in Neuroscience.
[9] Giulio Cimini,et al. The physics of financial networks , 2021, Nature Reviews Physics.
[10] Przemyslaw Biecek,et al. Local Interpretable Model-agnostic Explanations (LIME) , 2021 .
[11] N. Amoroso,et al. Predicting brain age with complex networks: From adolescence to adulthood , 2020, NeuroImage.
[12] V. Lapuente,et al. Sub-national Quality of Government in EU Member States: Presenting the 2021 European Quality of Government Index and its relationship with Covid-19 indicators , 2021 .
[13] Nicola Amoroso,et al. Economic Interplay Forecasting Business Success , 2021, Complex..
[14] Nicola Amoroso,et al. Potential energy of complex networks: a quantum mechanical perspective , 2020, Scientific Reports.
[15] N. Amoroso,et al. An equity-oriented rethink of global rankings with complex networks mapping development , 2020, Scientific Reports.
[16] Ester Pantaleo,et al. Identifying potential gene biomarkers for Parkinson’s disease through an information entropy based approach , 2020, Physical biology.
[17] Máxima Bolaños-Pizarro,et al. Complex networks for benchmarking in global universities rankings , 2020, Scientometrics.
[18] Gisbert Schneider,et al. Drug discovery with explainable artificial intelligence , 2020, Nature Machine Intelligence.
[19] Yee Whye Teh,et al. AI for social good: unlocking the opportunity for positive impact , 2020, Nature Communications.
[20] Paolo Giudici,et al. Explainable AI in Fintech Risk Management , 2020, Frontiers in Artificial Intelligence.
[21] Gary S Collins,et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness , 2020, BMJ.
[22] Chong You,et al. Rethinking Bias-Variance Trade-off for Generalization of Neural Networks , 2020, ICML.
[23] Hugh Chen,et al. From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.
[24] Jessen T. Havill,et al. Networks , 1995, Discovering Computer Science.
[25] Nicola Amoroso,et al. Shannon entropy approach reveals relevant genes in Alzheimer’s disease , 2019, PloS one.
[26] Leandro Medina,et al. Explaining the Shadow Economy in Europe: Size, Causes and Policy Options , 2019, IMF Working Papers.
[27] Peter Flach,et al. Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward , 2019, AAAI.
[28] M. Ozer,et al. Social and juristic challenges of artificial intelligence , 2019, Palgrave Communications.
[29] Nicola Amoroso,et al. Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age , 2019, Front. Aging Neurosci..
[30] Ben W. Ansell. The Politics of Housing , 2019, Annual Review of Political Science.
[31] Michele Ceriotti,et al. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. , 2018, Journal of chemical theory and computation.
[32] Adrian Weller,et al. Transparency: Motivations and Challenges , 2019, Explainable AI.
[33] Giulio Cimini,et al. Unfolding the innovation system for the development of countries: coevolution of Science, Technology and Production , 2017, Scientific Reports.
[34] Tim Miller,et al. Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..
[35] Nicola Amoroso,et al. Multiplex Networks for Early Diagnosis of Alzheimer's Disease , 2018, Front. Aging Neurosci..
[36] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[37] Ginestra Bianconi,et al. Multilayer Networks , 2018, Oxford Scholarship Online.
[38] Ginestra Bianconi,et al. Multilayer Networks , 2018, Oxford Scholarship Online.
[39] Piotr Sapiezynski,et al. Evidence for a conserved quantity in human mobility , 2016, Nature Human Behaviour.
[40] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[41] F. Caccioli,et al. Pathways towards instability in financial networks , 2016, Nature Communications.
[42] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[43] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[44] Claudia Olivetti,et al. The Evolution of Gender Gaps in Industrialized Countries , 2016, SSRN Electronic Journal.
[45] Un Desa. Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .
[46] D. Helbing,et al. Saving Human Lives: What Complexity Science and Information Systems can Contribute , 2014, Journal of Statistical Physics.
[47] Damien R. Farine,et al. Measuring phenotypic assortment in animal social networks: weighted associations are more robust than binary edges , 2014, Animal Behaviour.
[48] Francisco Pedroche,et al. A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues , 2013, Chaos.
[49] Guido Caldarelli,et al. A New Metrics for Countries' Fitness and Products' Complexity , 2012, Scientific Reports.
[50] G. Caldarelli,et al. DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk , 2012, Scientific Reports.
[51] Olaf Sporns,et al. THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.
[52] Jacob Feldman,et al. Conceptual complexity and the bias/variance tradeoff , 2011, Cognition.
[53] Witold R. Rudnicki,et al. Feature Selection with the Boruta Package , 2010 .
[54] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[55] V. Traag,et al. Community detection in networks with positive and negative links. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[56] Glenn Fung,et al. On the Dangers of Cross-Validation. An Experimental Evaluation , 2008, SDM.
[57] César A. Hidalgo,et al. The Product Space Conditions the Development of Nations , 2007, Science.
[58] J. Reichardt,et al. Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[59] M. Newman. Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[60] Cullen Schaffer,et al. Selecting a classification method by cross-validation , 1993, Machine Learning.
[61] D. G. Beech,et al. The Advanced Theory of Statistics. Volume 2: Inference and Relationship. , 1962 .