Abstract. We consider the issue of exploiting symmetries in the hierarchy of semidefinite programming relaxations recently introduced in polynomial optimization. We focus on probl...
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...
Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions, we p...
We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseud...
Translating digital signal processing (DSP) software into its finite-precision hardware implementation is often a timeconsuming task. We describe a new static analysis technique ...