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549 records from EconBiz based on author Name
1. Fair learning with bagging
Fermanian, Jean-David; Guégan, Dominique;2021
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link Link
2. Fair learning with bagging
abstractThe central question of this paper is how to enhance supervised learning algorithms with fairness requirement ensuring that any sensitive input does not "`unfairly"' influence the outcome of the learning algorithm. To attain this objective we proceed by three steps. First after introducing several notions of fairness in a uniform approach, we introduce a more general notion through conditional fairness definition which englobes most of the well known fairness definitions. Second we use a ensemble of binary and continuous classifiers to get an optimal solution for a fair predictive outcome using a related-post-processing procedure without any transformation on the data, nor on the training algorithms. Finally we introduce several tests to verify the fairness of the predictions. Some empirics are provided to illustrate our approach
Fermanian, Jean-David; Guégan, Dominique;2022
Availability: Link Link
3. Estimating lower tail dependence between pairs of poverty dimensions in Europe
D'Agostino, Antonella; De Luca, Giovanni; Guégan, Dominique;2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link
Citations: 1 (based on OpenCitations)
4. A note on the interpretability of machine learning algorithms
Guégan, Dominique;2020
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability:
Link 5. The other side of the coin : risks of the Libra Blockchain
Abraham, Louis; Guégan, Dominique;2019
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability:
Link 6. Artificial intelligence, data, ethics : an holistic approach for risks and regulation
Bogroff, Alexis; Guégan, Dominique;2019
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link
7. A Note on the Interpretability of Machine Learning Algorithms
abstractWe are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the “How”, i.e., how a black box or a very complex algorithm works, and the “Why”, i.e. why an algorithm produces such a result. These questions appeal to many actors, users, professions, regulators among others. Using a formal standardized framework, we indicate the solutions that exist by specifying which elements of the supply chain are impacted when we provide answers to the previous questions. This presentation, by standardizing the notations, allows to compare the different approaches and to highlight the specificities of each of them: both their objective and their process. The study is not exhaustive and the subject is far from being closed
Guégan, Dominique;2021
Availability: Link Link
Citations: 1 (based on OpenCitations)
8. Credit risk analysis using machine and deep learning models
abstractDue to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises. The estimation of future sea level rise (SLR) is a major concern for cities near coastlines and river systems. Despite this, current modelling underestimates the future risks of SLR to property. Direct risks posed to property include inundation, loss of physical property and associated economic and social costs. It is also crucial to consider the risks that emerge from scenarios after SLR. These may produce one-off or periodic events that will inflict physical, economic and social implications, and direct, indirect and consequential losses. Using a case study approach, this paper combines various forms of data to examine the implications of future SLR to further understand the potential risks. The research indicates that the financial implications for local government will be loss of rates associated with total property loss and declines in value. The challenges identified are not specific to this research. Other municipalities worldwide experience similar barriers (i.e., financial implications, coastal planning predicaments, data paucity, knowledge and capacity, and legal and political challenges). This research highlights the need for private and public stakeholders to co-develop and implement strategies to mitigate and adapt property to withstand the future challenges of climate change and SLR.
Addo, Peter Martey; Guégan, Dominique; Hassani, Bertrand;2018
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link Link
Citations: 103 (based on OpenCitations)
9. Nonparametric forecasting of multivariate probability density functions
Guégan, Dominique; Iacopini, Matteo;2018
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link
10. Is the bitcoin rush over?
Frunza, Marius-Cristian; Guégan, Dominique;2018
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link