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All publications by Ryan Adams
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Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat and Ryan Adams
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Celeste: Variational inference for a generative model of astronomical images
Jeffrey Regier, Andrew Miller, Jon Mcauliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel and Mr Prabhat
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud and Ryan Adams
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Input Warping for Bayesian Optimization of Non-Stationary Functions
Jasper Snoek, Kevin Swersky, Rich Zemel and Ryan Adams
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Learning Ordered Representations with Nested Dropout
Oren Rippel, Michael Gelbart and Ryan Adams
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Discovering Latent Network Structure in Point Process Data
Scott Linderman and Ryan Adams
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Learning the Parameters of Determinantal Point Process Kernels
Raja H. Affandi, Emily Fox, Ryan Adams and Ben Taskar
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Andrew Miller, Luke Bornn, Ryan Adams and Kirk Goldsberry
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Gaussian Process Kernels for Pattern Discovery and Extrapolation
Andrew Wilson and Ryan Adams
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013