Unifying distillation and privileged information

1 code implementation • 11 Nov 2015 • David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik

Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines.

Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution

no code implementations • 3 Jun 2013 • Vladimir Vapnik, Igor Braga, Rauf Izmailov

We introduce a general constructive setting of the density ratio estimation problem as a solution of a (multidimensional) integral equation.

Density Ratio Estimation

On the Theory of Learnining with Privileged Information

no code implementations • NeurIPS 2010 • Dmitry Pechyony, Vladimir Vapnik

In Learning Using Privileged Information (LUPI) paradigm, along with the standard training data in the decision space, a teacher supplies a learner with the privileged information in the correcting space.

Support Vector Clustering

1 code implementation • JMLR 2001 • Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik

We present a novel clustering method using the approach of support vector machines.

Clustering

A Support Vector Method for Clustering

1 code implementation • NeurIPS 2000 • Asa Ben-Hur, David Horn, Hava Siegelmann, Vladimir Vapnik

Data points enclosed by each contour are defined as a cluster.

Clustering