LAFI 2022
as the organisers put it
NEWS LAFI 2022 will be a purely virtual event. Detailed schedule to be posted soon. This workshop aims to bring programming-language and machine-learning researchers together to advance all aspects of languages for inference. Topics include but are not limited to: Design of programming languages for statistical inference and/or differentiable programming Inference algorithms for probabilistic programming languages, including ones that incorporate automatic differentiation Automatic differentiation algorithms for differentiable programming languages Probabilistic generative modelling and ...
who it's for
probabilistic programming researchers, programming language researchers, machine learning researchers, inference engineers, scientific computing engineers, statisticians working on Bayesian inference
topics
automatic differentiation for higher-order languages; denotational semantics of AD; probabilistic programming semantics with quasi-Borel spaces; stateful memoization in probabilistic programs; amortized inference for scientific simulators; involutive trans-dimensional MCMC; smoothed semantics for stochastic gradient descent; learning domain-specific probabilistic programs; rigorous interval-trace posterior approximation; compilation to probabilistic circuits and variable elimination
| title | author | length |
|---|---|---|
| Abstract types in probabilistic programming | Sam Staton | 59m |
Library called lazy people implements higher-order probabilistic types and runs Metropolis–Hastings to do regression: a rand_linear prior draws slope and… | ||
| Probabilistic and Differentiable Programming in Scientific Simulators | Atilim Gunes Baydin | 32m |
A framework connects existing stochastic simulators to probabilistic engines using a lightweight flatbuffers-based PPX protocol and a Python library (piperop)… | ||
| Stateful processes in probabilistic programming | Hugo Paquet | 31m |
Shows how Polya urn, Gaussian process and Indian Buffet Process implementations rely on explicit local state, stochastic memoization and countable duplication… | ||
| Programming Languages for Automatic Differentiation: What Now? | Damiano Mazza | 36m |
Claims that AD is a functor and that backprop is higher-order drive a survey of recent papers, formal correctness proofs, and a lambda-calculus style program… | ||
| Scalable structure learning and inference for domain-specific probabilistic prog | Feras A. Saad | 35m |
Claims an end-to-end pipeline that automatically learns domain-specific probabilistic programs for time series, tabular and relational data and compiles them… | ||
| Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic | Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka | 19m |
Extends Lee et al. (2020) to a higher-order, recursive, probabilistic language and proves every program denotes omega-PAP functions and that AD yields correct… | ||
| A Language and Smoothed Semantics for Convergent Stochastic Gradient Descent | Dominik Wagner, C.-H. Luke Ong | 18m |
Provides a smoothed denotational semantics (in Frölicher spaces) plus a mild type system forbidding parameter-dependent guards that ensures any typable… | ||
| Nonparametric Involutive Markov Chain Monte Carlo | Carol Mak, Fabian Zaiser, C.-H. Luke Ong | 20m |
Framework generalizes involutive MCMC to universal PPLs and implements a concrete fix for trans‑dimensional moves: it appends samples until proposal has… | ||
| Rigorous Approximation of Posterior Inference for Probabilistic Programs | Fabian Zaiser, Raven Beutner, C.-H. Luke Ong | 20m |
Claims rigorous, nonstochastic posterior bounds for a universal probabilistic language that allows recursion, continuous distributions and conditioning. Method… | ||
| Program Analysis of Probabilistic Programs | Maria I. Gorinova | 44m |
Builds a blockless Stan called SlickStan and gives the first formal deterministic semantics for Stan, then uses an information-flow typing pass to… | ||
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