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

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

International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Jiaxin Zhang
Sirui Bi
Guannan Zhang
ArXiv (abs)PDFHTML

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

6 / 6 papers shown
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
Fengyi Li
Ayoub Belhadji
Youssef Marzouk
204
4
0
23 Feb 2024
Modern Bayesian Experimental Design
Modern Bayesian Experimental DesignStatistical Science (Statist. Sci.), 2023
Tom Rainforth
Adam Foster
Desi R. Ivanova
Freddie Bickford-Smith
358
173
0
28 Feb 2023
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
Accelerating Inverse Learning via Intelligent Localization with Exploratory SamplingAAAI Conference on Artificial Intelligence (AAAI), 2022
Jiaxin Zhang
Sirui Bi
Victor Fung
285
3
0
02 Dec 2022
A General Recipe for Likelihood-free Bayesian Optimization
A General Recipe for Likelihood-free Bayesian OptimizationInternational Conference on Machine Learning (ICML), 2022
Jiaming Song
Lantao Yu
Willie Neiswanger
Stefano Ermon
330
29
0
27 Jun 2022
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models
Vincent Lim
Ellen R. Novoseller
Jeffrey Ichnowski
Huang Huang
Ken Goldberg
OffRL
242
15
0
08 Mar 2022
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
Steven Kleinegesse
Michael U. Gutmann
FedML
158
33
0
10 May 2021