The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to digest …

Deep generative models have achieved impressive success in recent years. Generative
Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for …

Generic generation and manipulation of text is challenging and has limited success compared
to recent deep generative modeling in visual domain. This paper aims at generating …

We report on the successful application of feature selection methods to a classification
problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our …

Convolutional Neural Networks (CNN) conduct image classification by activating dominant
features that correlated with labels. When the training and testing data are under similar …

We introduce scalable deep kernels, which combine the structural properties of deep
learning architectures with the non-parametric flexibility of kernel methods. Specifically, we …

We propose a parameter server system for distributed ML, which follows a Stale Synchronous
Parallel (SSP) model of computation that maximizes the time computational workers …

Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks)
is a challenging problem since the search space of DAGs is combinatorial and scales …

The balanced weighting scheme of tree lasso and additional experimental results. We prove
that the weighting scheme of the tree-lasso penalty achieves a balanced penalization of all …

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function
f which is only accessible via point evaluations. It is typically used in settings where f is …