Neural Variational Cut Posteriors without Upstream Data

arXiv:2510.10268 · stat.ML, cs.LG, stat.ME · Submitted 2025-10-11 · Read on arXiv

stat.ML, cs.LG, stat.ME

Submitted: 2025-10-11

Updated: 2026-09-02

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Normalizing Flow — 45× in this paper · explained in 3 episodes
• Normalizing Flows — 41× in this paper · explained in 4 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Bayesian Inference — 11× in this paper · explained in 5 episodes
• Neural Spline Flows — 10× in this paper
• Calibration — 9× in this paper · explained in 7 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Unconstrained Monotonic Neural Networks — 6× in this paper
• Copula — 5× in this paper · explained in 2 episodes
• Wasserstein Distance — 5× in this paper · explained in 4 episodes
• Core — 4× in this paper · explained in 2 episodes
• Uncertainty Quantification — 4× in this paper · explained in 8 episodes
• Adam — 3× in this paper · explained in 2 episodes
• As in RQ-NSF — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Continuous Ranked Probability Score — 2× in this paper
• Decode — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Amortising over hyperparameters in Generalised Bayesian Inference
• Scalable Semi-Modular Inference with Variational Meta-Posteriors
• Better together? Statistical learning in models made of modules
• Asymptotics of cut distributions and robust modular inference using Posterior Bootstrap
• Word Representations via Gaussian Embedding

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