Detecting influential features in complex (non-linear and/or high-dimensional) datasets is key for extracting the relevant information. Most of the popular selection procedures, however, require assumptions on the underlying data - such as distributional ones -, which barely agree with empirical observations. Therefore, feature selection based on nonlinear methods, such as the model-free HSIC-Lasso, is a more relevant approach. In order to ensure valid inference among the chosen features, the selection procedure must be accounted for. In this paper, we propose selective inference with HSIC-Lasso using the framework of truncated Gaussians together with the polyhedral lemma. Based on these theoretical foundations, we develop an algorithm allowing for low computational costs and the treatment of the hyper-parameter selection issue. The relevance of our method is illustrated using artificial and real-world datasets. In particular, our empirical findings emphasise that type-I error control at the considered level can be achieved.
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