Max Huber

Martina Huber KTH report

Anisotropic Huber-L1 Optical Flow

TV regularization is an L1 penalization of the flow gradient magnitudes, and due to the tendency of the L1 norm to favor sparse solutions (i.e. lots of ‘zeros’), the fill-in effect caused by the regularizer leads to piecewise constant solutions in weakly textured areas. This effect, known as ‘staircasing’ in a 1D setting, can be reduced significantly by using a quadratic penalization for small ...

Huber Publishers Berne Stuttgart Toronto

The "International Journal for Vitamin and Nutri t ion Research" is published at irregular intervals (about 4 fasc. per annum) 4 fasc. = 1 volume, prize per volume= Sw.Fr. 134.— / Die «Internat ionale Zeitschrift für Vitaminund Ernährungsforschung» erscheint in zwangloser Reihenfolge (ca. 4 Hefte pro Jahr). 4 Hefte ergeben einen Band. Bezugspreis pro Band = sFr. 134.—, D M 170.— / Le «Journa l ...

Huber-Norm Regularization for Linear Prediction Models

In order to avoid overfitting, it is common practice to regularize linear prediction models using squared or absolute-value norms of the model parameters. In our article we consider a new method of regularization: Huber-norm regularization imposes a combination of `1 and `2-norm regularization on the model parameters. We derive the dual optimization problem, prove an upper bound on the statisti...

Active Regression with Adaptive Huber Loss

This paper addresses the scalar regression problem presenting a solution for optimizing the Huber loss in a general semi-supervised setting, which combines multi-view learning and manifold regularization. To this aim, we propose a principled algorithm to 1) avoid computationally expensive iterative solutions while 2) adapting the Huber loss threshold in a data-driven fashion and 3) actively bal...