1,300 research outputs found
Asynchronous Probabilistic Couplings in Higher-Order Separation Logic
Probabilistic couplings are the foundation for many probabilistic relational
program logics and arise when relating random sampling statements across two
programs. In relational program logics, this manifests as dedicated coupling
rules that, e.g., say we may reason as if two sampling statements return the
same value. However, this approach fundamentally requires aligning or
"synchronizing" the sampling statements of the two programs which is not always
possible.
In this paper, we develop Clutch, a higher-order probabilistic relational
separation logic that addresses this issue by supporting asynchronous
probabilistic couplings. We use Clutch to develop a logical step-indexed
logical relational to reason about contextual refinement and equivalence of
higher-order programs written in a rich language with higher-order local state
and impredicative polymorphism. Finally, we demonstrate the usefulness of our
approach on a number of case studies.
All the results that appear in the paper have been formalized in the Coq
proof assistant using the Coquelicot library and the Iris separation logic
framework
Step-Indexed Logical Relations for Probability (long version)
It is well-known that constructing models of higher-order probabilistic
programming languages is challenging. We show how to construct step-indexed
logical relations for a probabilistic extension of a higher-order programming
language with impredicative polymorphism and recursive types. We show that the
resulting logical relation is sound and complete with respect to the contextual
preorder and, moreover, that it is convenient for reasoning about concrete
program equivalences. Finally, we extend the language with dynamically
allocated first-order references and show how to extend the logical relation to
this language. We show that the resulting relation remains useful for reasoning
about examples involving both state and probabilistic choice.Comment: Extended version with appendix of a FoSSaCS'15 pape
A Domain Theory for Statistical Probabilistic Programming
We give an adequate denotational semantics for languages with recursive higher-order types, continuous probability distributions, and soft constraints. These are expressive languages for building Bayesian models of the kinds used in computational statistics and machine learning. Among them are untyped languages, similar to Church and WebPPL, because our semantics allows recursive mixed-variance datatypes. Our semantics justifies important program equivalences including commutativity. Our new semantic model is based on `quasi-Borel predomains'. These are a mixture of chain-complete partial orders (cpos) and quasi-Borel spaces. Quasi-Borel spaces are a recent model of probability theory that focuses on sets of admissible random elements. Probability is traditionally treated in cpo models using probabilistic powerdomains, but these are not known to be commutative on any class of cpos with higher order functions. By contrast, quasi-Borel predomains do support both a commutative probabilistic powerdomain and higher-order functions. As we show, quasi-Borel predomains form both a model of Fiore's axiomatic domain theory and a model of Kock's synthetic measure theory.</p
On Probabilistic Applicative Bisimulation and Call-by-Value -Calculi (Long Version)
Probabilistic applicative bisimulation is a recently introduced coinductive
methodology for program equivalence in a probabilistic, higher-order, setting.
In this paper, the technique is applied to a typed, call-by-value,
lambda-calculus. Surprisingly, the obtained relation coincides with context
equivalence, contrary to what happens when call-by-name evaluation is
considered. Even more surprisingly, full-abstraction only holds in a symmetric
setting.Comment: 30 page
Semantic Composition via Probabilistic Model Theory
Semantic composition remains an open problem for vector space models of semantics. In this paper, we explain how the probabilistic graphical model used in the framework of Functional Distributional Semantics can be interpreted as a probabilistic version of model theory. Building on this, we explain how various semantic phenomena can be recast in terms of conditional probabilities in the graphical model. This connection between formal semantics and machine learning is helpful in both directions: it gives us an explicit mechanism for modelling context-dependent meanings (a challenge for formal semantics), and also gives us well-motivated techniques for composing distributed representations (a challenge for distributional semantics). We present results on two datasets that go beyond word similarity, showing how these semantically-motivated techniques improve on the performance of vector models.Schiff Foundatio
Foundations of Software Science and Computation Structures
This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.
Probabilistic Programming Interfaces for Random Graphs::Markov Categories, Graphons, and Nominal Sets
We study semantic models of probabilistic programming languages over graphs, and establish a connection to graphons from graph theory and combinatorics. We show that every well-behaved equational theory for our graph probabilistic programming language corresponds to a graphon, and conversely, every graphon arises in this way.We provide three constructions for showing that every graphon arises from an equational theory. The first is an abstract construction, using Markov categories and monoidal indeterminates. The second and third are more concrete. The second is in terms of traditional measure theoretic probability, which covers 'black-and-white' graphons. The third is in terms of probability monads on the nominal sets of Gabbay and Pitts. Specifically, we use a variation of nominal sets induced by the theory of graphs, which covers Erdős-Rényi graphons. In this way, we build new models of graph probabilistic programming from graphons
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