An IPM-based framework for Bayesian optimal experimental design is proposed that replaces KL-based expected information gain with Wasserstein, MMD, and energy distances, delivering stronger stability guarantees and plug-and-play extensions.
PNOD: An efficient projected Newton framework for exact optimal experimental designs.https://arxiv.org/abs/2409.18392
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
An IPM-based framework for Bayesian optimal experimental design is proposed that replaces KL-based expected information gain with Wasserstein, MMD, and energy distances, delivering stronger stability guarantees and plug-and-play extensions.