June 11, 2024, 4:48 a.m. | Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.14254v2 Announce Type: replace
Abstract: Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal set of informative features for the entire population. However, feature relevance often varies with context, while the context itself may not directly affect the outcome variable. Here, we propose a novel architecture for contextual feature selection where the subset of selected features is conditioned on the value of context variables. Our …

abstract arxiv context cs.lg cs.ne feature features feature selection gates however identify machine machine learning population replace scientific set stochastic tool type universal while

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