33 citations · 182 across the 20 of their papers we have counts for
5 papers · 1 filter
Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen +3
We consider the problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the v…
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen +1
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing.…
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen +2
Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intrac…
Preferential Batch Bayesian Optimization
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen +3
Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This dire…
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data
Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1
Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets…