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pro vyhledávání: '"Wehenkel, Louis"'
Autor:
Donon, Balthazar, Cubelier, François, Karangelos, Efthymios, Wehenkel, Louis, Crochepierre, Laure, Pache, Camille, Saludjian, Lucas, Panciatici, Patrick
Publikováno v:
In Electric Power Systems Research September 2024 234
Publikováno v:
35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output. On the other hand, methods based on S
Externí odkaz:
http://arxiv.org/abs/2111.02218
Autor:
Karangelos, Efthymios, Wehenkel, Louis
We model the risk posed by a malicious cyber-attacker seeking to induce grid insecurity by means of a load redistribution attack, while explicitly acknowledging that such an actor would plausibly base its decision strategy on imperfect information. M
Externí odkaz:
http://arxiv.org/abs/2110.00301
Publikováno v:
In Sustainable Energy, Grids and Networks March 2024 37
Autor:
Zhou, Kai, Cruise, James R., Dent, Chris J., Dobson, Ian, Wehenkel, Louis, Wang, Zhaoyu, Wilson, Amy L.
Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies
Externí odkaz:
http://arxiv.org/abs/2001.08681
In many applications of supervised learning, multiple classification or regression outputs have to be predicted jointly. We consider several extensions of gradient boosting to address such problems. We first propose a straightforward adaptation of gr
Externí odkaz:
http://arxiv.org/abs/1905.07558
Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-relate
Externí odkaz:
http://arxiv.org/abs/1801.00500
Autor:
Karangelos, Efthymios, Wehenkel, Louis
Publikováno v:
In Electric Power Systems Research October 2022 211
Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory is not large enough to contain all features. In this setting, we propo
Externí odkaz:
http://arxiv.org/abs/1709.01177
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