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Practical Bayesian Optimization of Machine Learning Algorithms

• Jasper Snoek, Hugo Larochelle, Ryan P. Adams
• NIPS
• 2012

Bayesian Online Changepoint Detection

• Ryan P. Adams
• 2006

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as… (More)

Taking the Human Out of the Loop: A Review of Bayesian Optimization

• Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas
• Proceedings of the IEEE
• 2016

Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The… (More)

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

• José Miguel Hernández-Lobato, Ryan P. Adams
• ICML
• 2015

Large multilayer neural networks trained with backpropagation have recently achieved state-ofthe-art results in a wide range of problems. However, using backprop for neural net learning still has… (More)

Convolutional Networks on Graphs for Learning Molecular Fingerprints

• David K. Duvenaud, Dougal Maclaurin, +4 authors Ryan P. Adams
• NIPS
• 2015

Elliptical slice sampling

• Iain Murray, Ryan P. Adams, David J. C. MacKay
• AISTATS
• 2010

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm… (More)

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

• Ryan P. Adams, Iain Murray, David J. C. MacKay
• ICML
• 2009

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to… (More)

Gradient-based Hyperparameter Optimization through Reversible Learning

• Dougal Maclaurin, David K. Duvenaud, Ryan P. Adams
• ICML
• 2015

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by… (More)

Multi-Task Bayesian Optimization

• Kevin Swersky, Jasper Snoek, Ryan P. Adams
• NIPS
• 2013

Bayesian optimization has recently been proposed as a framework for automatically tuning the hyperparameters of machine learning models and has been shown to yield state-of-the-art performance with… (More)

Tree-Structured Stick Breaking for Hierarchical Data

• Ryan P. Adams, Zoubin Ghahramani, Michael I. Jordan
• NIPS
• 2010

Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking… (More)

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