How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

Deterministic neural nets have been shown to learn effective predictors on a wide range of
machine learning problems. However, as the standard approach is to train the network to …

Bayesian neural networks with latent variables are scalable and flexible probabilistic
models: they account for uncertainty in the estimation of the network weights and, by making …

Abstract Gaussian Process Latent Variable Model (GPLVM), as a flexible bayesian non-
parametric modeling method, has been extensively studied and applied in many learning …

Large multilayer neural networks trained with backpropagation have recently achieved state-
of-the-art results in a wide range of problems. However, using backprop for neural net …

We propose a novel information-theoretic approach for Bayesian optimization called
Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that …

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a
target matrix, which is critically important in collaborative filtering (CF)-based recommender …

We present\small PESMO, a Bayesian method for identifying the Pareto set of multi-objective
optimization problems, when the functions are expensive to evaluate.\small PESMO …

We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm …

Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …