Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation

arXiv:2609.38902 · cs.LG, stat.ML · Submitted 2026-09-30 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/ChinHungScott/NPE-for-Bayesian-Dynamic-Borrowing-MLHC-

Terminology

• Exchangeability — 52× in this paper · explained in 2 episodes
• Covariate Shift — 12× in this paper · explained in 2 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Simulation-based Inference — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Amortized Neural Posterior Estimation (NPE) — 2× in this paper · explained in 1 episode
• Causal Inference — 2× in this paper · explained in 4 episodes
• Neural posterior estimation (NPE) — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hierarchical Framework — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Simulation-Based Inference (SBI) — 1× in this paper · explained in 3 episodes

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