Differentiable Feature Selection by Discrete Relaxation

Rishit Sheth, Nicoló Fusi
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1564-1572, 2020.

Abstract

In this paper, we introduce Differentiable Feature Selection, a gradient-based search algorithm for feature selection. Our approach extends a recent result on the estimation of learnability in the sublinear data regime by showing that the calculation can be performed iteratively (i.e. in mini-batches) and in linear time and space with respect to both the number of features D and the sample size N. This, along with a discrete-to-continuous relaxation of the search domain, allows for an efficient, gradient-based search algorithm among feature subsets for very large datasets. Our algorithm utilizes higher-order correlations between features and targets for both the N>D and N

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-sheth20a, title = {Differentiable Feature Selection by Discrete Relaxation}, author = {Sheth, Rishit and Fusi, Nicol\'o}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1564--1572}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/sheth20a/sheth20a.pdf}, url = { http://proceedings.mlr.press/v108/sheth20a.html }, abstract = {In this paper, we introduce Differentiable Feature Selection, a gradient-based search algorithm for feature selection. Our approach extends a recent result on the estimation of learnability in the sublinear data regime by showing that the calculation can be performed iteratively (i.e. in mini-batches) and in linear time and space with respect to both the number of features D and the sample size N. This, along with a discrete-to-continuous relaxation of the search domain, allows for an efficient, gradient-based search algorithm among feature subsets for very large datasets. Our algorithm utilizes higher-order correlations between features and targets for both the N>D and N
Endnote
%0 Conference Paper %T Differentiable Feature Selection by Discrete Relaxation %A Rishit Sheth %A Nicoló Fusi %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-sheth20a %I PMLR %P 1564--1572 %U http://proceedings.mlr.press/v108/sheth20a.html %V 108 %X In this paper, we introduce Differentiable Feature Selection, a gradient-based search algorithm for feature selection. Our approach extends a recent result on the estimation of learnability in the sublinear data regime by showing that the calculation can be performed iteratively (i.e. in mini-batches) and in linear time and space with respect to both the number of features D and the sample size N. This, along with a discrete-to-continuous relaxation of the search domain, allows for an efficient, gradient-based search algorithm among feature subsets for very large datasets. Our algorithm utilizes higher-order correlations between features and targets for both the N>D and N
APA
Sheth, R. & Fusi, N.. (2020). Differentiable Feature Selection by Discrete Relaxation. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1564-1572 Available from http://proceedings.mlr.press/v108/sheth20a.html .

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