Simplifying dependent reductions in the polyhedral model

C Yang, E Atkinson, M Carbin - … of the ACM on Programming Languages, 2021 - dl.acm.org
A Reduction–an accumulation over a set of values, using an associative and commutative
operator–is a common computation in many numerical computations, including scientific …

Sound probabilistic inference via guide types

D Wang, J Hoffmann, T Reps - Proceedings of the 42nd ACM SIGPLAN …, 2021 - dl.acm.org
Probabilistic programming languages aim to describe and automate Bayesian modeling and
inference. Modern languages support programmable inference, which allows users to …

Statically bounded-memory delayed sampling for probabilistic streams

E Atkinson, G Baudart, L Mandel, C Yuan… - Proceedings of the ACM …, 2021 - dl.acm.org
Probabilistic programming languages aid developers performing Bayesian inference. These
languages provide programming constructs and tools for probabilistic modeling and …

Overparameterization: A connection between software 1.0 and software 2.0

M Carbin - 3rd Summit on Advances in Programming Languages …, 2019 - drops.dagstuhl.de
A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen
that integrates neural networks into a variety of computational tasks. Such applications …

Gen: a high-level programming platform for probabilistic inference

MF Cusumano-Towner - 2020 - dspace.mit.edu
Probabilistic inference provides a powerful theoretical framework for engineering intelligent
systems. However, diverse modeling approaches and inference algorithms are needed to …

Simplifying multiple-statement reductions with the polyhedral model

JJC Yang - 2020 - dspace.mit.edu
Reduction--an accumulation over a set of values, using an associative and commutative
operator--is a common computation in many numerical computations, including scientific …

[PDF][PDF] Automatic Code Generation for Statistical Models with Augmentation and Collapsing.

S Seneviratne - 2021 - scholar.archive.org
With the advent of data science, statistical modelling has become increasingly popular over
the past decade. Probabilistic programming languages contrive to make such models …

Approximations in Probabilistic Programs

E Sharma, DM Roy - arxiv preprint arxiv:1912.06791, 2019 - arxiv.org
We study the first-order probabilistic programming language introduced by Staton et
al.(2016), but with an additional language construct, $\mathbf {stat} $, that, like the fixpoint …