Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes

no code implementations • 9 Aug 2014 • Ryan Prescott Adams, George E. Dahl, Iain Murray

Probabilistic matrix factorization (PMF) is a powerful method for modeling data associ- ated with pairwise relationships, Finding use in collaborative Filtering, computational bi- ology, and document analysis, among other areas.

Collaborative Filtering Gaussian Processes

Freeze-Thaw Bayesian Optimization

1 code implementation • 16 Jun 2014 • Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings.

Bayesian Optimization BIG-bench Machine Learning

Gaussian Process Kernels for Pattern Discovery and Extrapolation

2 code implementations • 18 Feb 2013 • Andrew Gordon Wilson, Ryan Prescott Adams

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation.

Gaussian Processes

Ranking via Sinkhorn Propagation

no code implementations • 9 Jun 2011 • Ryan Prescott Adams, Richard S. Zemel

It is of increasing importance to develop learning methods for ranking.

Information Retrieval Retrieval

Elliptical slice sampling

1 code implementation • 31 Dec 2009 • Iain Murray, Ryan Prescott Adams, David J. C. MacKay

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process.

Bayesian Online Changepoint Detection

7 code implementations • 19 Oct 2007 • Ryan Prescott Adams, David J. C. MacKay

Changepoints are abrupt variations in the generative parameters of a data sequence.

Change Point Detection Time Series +1