Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.

Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129

We investigate several reinforcement learning algorithms on three Bayesian experimental design problems. Performance is measured by each agent's training time and generalisability to various experimental setups at evaluation time.

Code for paper "Bayesian I-optimal designs for choice experiments with mixtures" by Mario Becerra and Peter Goos.

Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html

Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379

Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912

Implementation of Bayesian experimental design using regularized determinantal point processes

Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.