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Programmable MCMC with Soundly Composed Guide Programs

Talkat OOPSLA 2024 · 2024-10-20..10-25· [machine-learning]
name:Long Pham
links:none on file
name:Di Wang
links:none on file
links:none on file
links:none on file
slides:none on file

Implements a coroutine-based framework and a type system that encodes the message protocol between a model program and a sequence of block-wise guide programs, translates guide types to context-free processes with finite norms, and leverages a 1994 decidability result to achieve polynomial-time structural equality checks. Adds a coverage-checking algorithm that statically verifies every latent variable is freshly sampled in the guide composition; evaluation on prior benchmarks shows fast type inference and detection of unsound guides for finite-state cases but notes possible false negatives when guide types have infinite state spaces. Artifacts and reproduction data are provided.

AI-written summary · about

  1. 0:00Introduction
  2. 1:45Bayesian polynomial regression
  3. 3:30Limitations of inference
  4. 4:36Blockwise Metropolis-Hastings
  5. 6:42BMH soundness condition
  6. 7:47Static verification of BMH
  7. 8:30Sequential composition
  8. 12:29Type equality check
  9. 13:32Guide program coverage
  10. 15:37Implementation results
  11. 17:01Summary
  12. 17:47Q&A
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