Bayesian Nonlinear Support Vector Machines for Big Data


We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.

European Conference on Machine Learning (ECML)
Best Student Paper Award Nomination Oral Presentation

More Publications