Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance
cs.LG
Submitted: 2026-01-23
Updated: 2026-08-28
Terminology
Sources
- On integral probability metrics, \phi-divergences and binary classification
- Wasserstein GAN
- Wasserstein Auto-Encoders
- Certifying Some Distributional Robustness with Principled Adversarial Training
- Bayesian Active Learning for Classification and Preference Learning
- Bayesian optimal experimental design with Wasserstein information criteria
- On the 1-Wasserstein Distance between Location-Scale Distributions and the Effect of Differential Privacy
- Relative Translation Invariant Wasserstein Distance
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks