Sam Staton | DeepAI

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∙ 09/18/2023

A model of stochastic memoization and name generation in probabilistic programming: categorical semantics via monads on presheaf categories

Stochastic memoization is a higher-order construct of probabilistic prog...

0 Younesse Kaddar, et al. ∙

∙ 02/21/2023

ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs

We introduce a new setting, the category of ωPAP spaces, for reasoning d...

0 Mathieu Huot, et al. ∙

∙ 12/14/2022

Affine Monads and Lazy Structures for Bayesian Programming

We show that streams and lazy data structures are a natural idiom for pr...

0 Swaraj Dash, et al. ∙

∙ 12/13/2022

ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs

Optimizing the expected values of probabilistic processes is a central p...

0 Alexander K. Lew, et al. ∙

∙ 07/12/2022

Quantum de Finetti Theorems as Categorical Limits, and Limits of State Spaces of C*-algebras

De Finetti theorems tell us that if we expect the likelihood of outcomes...

0 Sam Staton, et al. ∙

∙ 05/31/2022

Concrete categories and higher-order recursion (With applications including probability, differentiability, and full abstraction)

We study concrete sheaf models for a call-by-value higher-order language...

0 Cristina Matache, et al. ∙

∙ 12/28/2021

Monads for Measurable Queries in Probabilistic Databases

We consider a bag (multiset) monad on the category of standard Borel spa...

0 Swaraj Dash, et al. ∙

∙ 05/05/2021

Recursion and Sequentiality in Categories of Sheaves

We present a fully abstract model of a call-by-value language with highe...

0 Cristina Matache, et al. ∙

∙ 01/27/2021

Compositional Semantics for Probabilistic Programs with Exact Conditioning

We define a probabilistic programming language for Gaussian random varia...

0 Dario Stein, et al. ∙

∙ 01/26/2021

A Monad for Probabilistic Point Processes

A point process on a space is a random bag of elements of that space. In...

0 Swaraj Dash, et al. ∙

∙ 01/17/2021

Higher Order Automatic Differentiation of Higher Order Functions

We present semantic correctness proofs of automatic differentiation (AD)...

0 Mathieu Huot, et al. ∙

∙ 07/16/2020

Probabilistic Programming Semantics for Name Generation

We make a formal analogy between random sampling and fresh name generati...

0 Marcin Sabok, et al. ∙

∙ 01/07/2020

Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing

We present semantic correctness proofs of Automatic Differentiation (AD)...

0 Mathieu Huot, et al. ∙

∙ 04/21/2019

Quantum channels as a categorical completion

We propose a categorical foundation for the connection between pure and ...

0 Mathieu Huot, et al. ∙

∙ 01/29/2019

Universal Properties in Quantum Theory

We argue that notions in quantum theory should have universal properties...

0 Mathieu Huot, et al. ∙

∙ 12/15/2018

A Generalization of Hierarchical Exchangeability on Trees to Directed Acyclic Graphs

Motivated by problems in Bayesian nonparametrics and probabilistic progr...

0 Paul Jung, et al. ∙

∙ 11/10/2018

A Domain Theory for Statistical Probabilistic Programming

We give an adequate denotational semantics for languages with recursive ...

0 Matthijs Vákár, et al. ∙

∙ 02/26/2018

The Beta-Bernoulli process and algebraic effects

In this paper we analyze the Beta-Bernoulli process from Bayesian nonpar...

0 Sam Staton, et al. ∙

∙ 11/14/2017

Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory

We describe categorical models of a circuit-based (quantum) functional p...

0 Mathys Rennela, et al. ∙

∙ 11/09/2017

Denotational validation of higher-order Bayesian inference

We present a modular semantic account of Bayesian inference algorithms f...

0 Adam Ścibior, et al. ∙

∙ 01/10/2017

A Convenient Category for Higher-Order Probability Theory

Higher-order probabilistic programming languages allow programmers to wr...

0 Chris Heunen, et al. ∙

∙ 01/19/2016

Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

We study the semantic foundation of expressive probabilistic programming...

0 Sam Staton, et al. ∙

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