A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models

14 March 2021

Papers citing "A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models"

15 / 15 papers shown

Title
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information Steven Kleinegesse Christopher C. Drovandi Michael U. Gutmann 111 28 0 20 Mar 2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation Steven Kleinegesse Michael U. Gutmann 54 66 0 19 Feb 2020
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization Jiaxin Zhang Hoang Tran Dan Lu Guannan Zhang 61 17 0 07 Feb 2020
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments Adam Foster M. Jankowiak M. O'Meara Yee Whye Teh Tom Rainforth BDL 67 61 0 01 Nov 2019
Understanding the Limitations of Variational Mutual Information Estimators Jiaming Song Stefano Ermon SSL DRL 59 203 0 14 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 86 464 0 03 Oct 2019
Formal Limitations on the Measurement of Mutual Information David A. McAllester K. Stratos SSL 63 275 0 10 Nov 2018
Efficient Bayesian Experimental Design for Implicit Models Steven Kleinegesse Michael U. Gutmann 62 50 0 23 Oct 2018
Guided evolutionary strategies: Augmenting random search with surrogate gradients Niru Maheswaranathan Luke Metz George Tucker Dami Choi Jascha Narain Sohl-Dickstein 51 20 0 26 Jun 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization K. Choromanski Mark Rowland Vikas Sindhwani Richard Turner Adrian Weller 66 149 0 06 Apr 2018
On Nesting Monte Carlo Estimators Tom Rainforth R. Cornish Hongseok Yang Andrew Warrington Frank Wood 91 132 0 18 Sep 2017
Curiosity-driven Exploration by Self-supervised Prediction Deepak Pathak Pulkit Agrawal Alexei A. Efros Trevor Darrell LRM SSL 106 2,437 0 15 May 2017
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning Zi Wang Chengtao Li Stefanie Jegelka Pushmeet Kohli 64 124 0 06 Mar 2017
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin Botond Cseke Ryota Tomioka GAN 151 1,655 0 02 Jun 2016
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 353 7,936 0 13 Jun 2012