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We introduce a new Bayesian multi-class support vector machine by formulatinga pseudo-likelihood for a multi-class hinge loss in the form of alocation-scale mixture of Gaussians. We derive a variational-inference-basedtraining objective for gradient-based learning. Additionally, we employ aninducing point approximation which scales inference to large data sets.Furthermore, we develop hybrid Bayesian neural networks that combine standarddeep learning components with the proposed model to enable learning forunstructured data. We provide empirical evidence that our model outperforms thecompetitor methods with respect to both training time and accuracy inclassification experiments on 68 structured and two unstructured data sets.Finally, we highlight the key capability of our model in yielding predictionuncertainty for classification by demonstrating its effectiveness in the tasksof large-scale active learning and detection of adversarial images.

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data