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<https://discovery.ucl.ac.uk/id/eprint/10174125> <http://purl.org/dc/terms/title> "Stochastic Optimization with Adaptive Batch Size:\r\nDiscrete Choice Models as a Case Study"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10174125> <http://purl.org/ontology/bibo/abstract> "The 2.5 quintillion bytes of data created each day brings new opportunities, but also new\r\nstimulating challenges for the discrete choice community. Opportunities because more and more\r\nnew and larger data sets will undoubtedly become available in the future. Challenging because\r\ninsights can only be discovered if models can be estimated, which is not simple on these large\r\ndatasets.\r\nIn this paper, inspired by the good practices and the intensive use of stochastic gradient methods\r\nin the ML field, we introduce the algorithm called Window Moving Average - Adaptive Batch\r\nSize (WMA-ABS) which is used to improve the efficiency of stochastic second-order methods.\r\nWe present preliminary results that indicate that our algorithms outperform the standard secondorder methods, especially for large datasets. It constitutes a first step to show that stochastic\r\nalgorithms can finally find their place in the optimization of Discrete Choice Models."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10174125> <http://purl.org/dc/terms/date> "2019-05" .
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