Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …

Semi-supervised learning is a learning paradigm concerned with the study of how
computers and natural systems such as humans learn in the presence of both labeled and …

We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

We define notions of stability for learning algorithms and show how to use these notions to
derive generalization error bounds based on the empirical error and the leave-one-out error …

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random
variables to infinite (countably or continuous) index sets. GPs have been applied in a large …

Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled
data set? We present a framework that simultaneously clusters the data and trains a …

In traditional machine learning approaches to classification, one uses only a labeled set to
train the classifier. Labeled instances however are often difficult, expensive, or time …

In this paper, we describe a statistical method for 3D object detection. We represent the
statistics of both object appearance and" non-object" appearance using a product of …