Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.
How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.