Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark

By Isabelle M Guyon, Jiwen Li, Theodor Mader, Patrick A. Pletscher, Georg Schneider, Markus Uhr. 2007

107 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Molecular Biology, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sebastian Pokutta
2. Damien Scieur
3. Thomas Kerdreux
4. Simon Lacoste-Julien
5. Alejandro Carderera
6. Lewis Liu
7. Romaric Gaudel
8. Michèle Sebag

In Information Systems

1. Simon Lacoste-Julien
2. Sebastian Pokutta
3. Michèle Sebag

In Molecular Biology

1. Simon Lacoste-Julien
2. Sebastian Pokutta
3. Michèle Sebag

In Evaluation & Benchmarks

1. Simon Lacoste-Julien
2. Sebastian Pokutta
3. Michèle Sebag

Citations in the corpus

• Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets (2020, background): “We clearly see that the directional smoothness parameter L f,δ is affine invariant, as its estimate is max k L k = 32 in both scenarios. back-tracking FW [Pedregosa et al. 2020] on the Made-lon dataset [Guyon et al. 2007].”
• Second-order Conditional Gradients (2020, methodology): “The labels and samples used are taken from the training set of the gissette (Guyon et al., 2007) dataset, where n = 5000 and m = 6000 .”
• Feature Selection as a One-Player Game (2010, background): “After (Guyon et al., 2007; Shen et al., 2008), Madelon and Colon are centered and normalized (based on the training set).”