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Testing MCMC code
Roger B. Grosse, David K. Duvenaud
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naive…
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
Kevin Swersky, David Duvenaud, Jasper Snoek +2
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures…
Warped Mixtures for Nonparametric Cluster Shapes
Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a mode…
Optimally-Weighted Herding is Bayesian Quadrature
Ferenc Huszar, David Duvenaud
Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals usi…
Probabilistic ODE Solvers with Runge-Kutta Means
Michael Schober, David Duvenaud, Philipp Hennig
Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return p…
Avoiding pathologies in very deep networks
David Duvenaud, Oren Rippel, Ryan P. Adams +1
Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogou…