Excellent variational approximations to Gaussian process posteriors have been developed
which avoid the $\mathcal {O}\left (N^ 3\right) $ scaling with dataset size $ N $. They reduce …

We give the first algorithm for kernel Nystrom approximation that runs in linear time in the
number of training points and is provably accurate for all kernel matrices, without …

Gaussian processes are distributions over functions that are versatile and mathematically
convenient priors in Bayesian modelling. However, their use is often impeded for data with …

Recent years have witnessed a resurgence of interest in video summarization. However,
one of the main obstacles to the research on video summarization is the user subjectivity …

We present a new algorithm for finding a near optimal low-rank approximation of a matrix A
in O (n nz (A)) time. Our method is based on a recursive sampling scheme for computing a …

We show fully polynomial time randomized approximation schemes (FPRAS) for counting
matchings of a given size, or more generally sampling/counting monomer-dimer systems in …

We analyze the Nyström approximation of a positive definite kernel associated with a
probability measure. We first prove an improved error bound for the conventional Nyström …

Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy
black box functions. One example in machine learning is hyper-parameter optimization …

The analysis of time series is becoming increasingly prevalent across scientific disciplines
and industrial applications. The effectiveness and the scalability of time-series mining …

We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While
classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a …