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These pages have attached code that can be downloaded.

Scalable Bayesian Optimization Using Deep Neural Networks

Scalable Bayesian Optimization Using Deep Neural Networks. Snoek, J, Rippel O, Swersky K, Kiros R, Satish N, Sundaram N, Patwary MMA, Prabhat, Adams RP.  Proceedings of the 32nd International Conference on Machine Learning. 2015.   { arXiv:1502.05700 [stat.ML] | PDF | Google Scholar | BibTex }

Gradient-based Hyperparameter Optimization through Reversible Learning

Gradient-based Hyperparameter Optimization through Reversible Learning. Maclaurin, D, Duvenaud D, Adams RP.  Proceedings of the 32nd International Conference on Machine Learning. 2015.   { arXiv:1502.03492 [stat.ML] | PDF | Code | Google Scholar | BibTex }

Bayesian Optimization with Unknown Constraints

Bayesian Optimization with Unknown Constraints. Gelbart, MA, Snoek J, Adams RP.  Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI). 2014.   { arXiv:1403.5607 [stat.ML] | PDF | Code | Google Scholar | BibTex }

Avoiding Pathologies in Very Deep Networks

Avoiding Pathologies in Very Deep Networks. Duvenaud, D, Rippel O, Adams RP, Ghahramani Z.  Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS). 2014.   { arXiv:1402.5836 [stat.ML] | PDF | Code | Google Scholar | BibTex }

Multi-Task Bayesian Optimization

Multi-Task Bayesian Optimization. Swersky, K, Snoek J, Adams RP.  Advances in Neural Information Processing Systems 26. 2013.   { PDF | Code | Google Scholar | BibTex }

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Gaussian Process Kernels for Pattern Discovery and Extrapolation. Wilson, AG, Adams RP.  Proceedings of the 30th International Conference on Machine Learning. 2013.   { arXiv:1302.4245 [stat.ML] | PDF | Code | Google Scholar | BibTex }