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Sat 23 Jan 2016 15:30 - 16:30 at Room St Petersburg II - Poster Session

In probabilistic programming languages, model construction and inference are separate tasks. The former is done by the language users and the latter by the language developers. Unfettered by concerns about inference, modelers will want to create big, complex probabilistic programs, which will present familiar problems: Models may become too big to comprehend, debug or validate as wholes. Users building large applications will need to divide and coordinate work among several people and will need to reuse common elements of models without rebuilding them. We hypothesize that model creation will benefit from two linguistic tools that have proven helpful in deterministic programming: types and modules.

With Insomnia, we hope to provide the best of two worlds: to help with the “democratization of machine learning,” we provide a language with abstractions for modular development of complex probabilistic programs. To help with analysis and understanding of models’ properties, we define the meaning of modular models by elaborating them into a small (module-free) core calculus whose semantics may be studied further.

Sat 23 Jan
Times are displayed in time zone: Guadalajara, Mexico City, Monterrey change

15:30 - 16:30: Poster SessionPPS at Room St Petersburg II
15:30 - 16:30
Meeting
Insomnia: Types and Modules for Probabilistic Programming
PPS
Aleksey KligerXamarin, Inc., Sean StromstenBAE Systems, Inc.
Pre-print
15:30 - 16:30
Meeting
Finite-depth Higher-order Abstract Syntax Trees for Reasoning about Probabilistic Programs
PPS
Theophilos GiannakopoulosBAE Systems, Inc., Mitchell WandNortheastern University, Andrew CobbNortheastern University
Pre-print
15:30 - 16:30
Meeting
Coalgebraic Trace Semantics for Probabilistic Processes: Preliminary Proposal
PPS
Larry MossIndiana University, Chung-chieh ShanIndiana University, Alexandra SilvaRadboud University Nijmegen
Pre-print
15:30 - 16:30
Meeting
Reasoning about Probability and Nondeterminism
PPS
Faris Abou-SalehUniversity of Oxford, Kwok-Ho CheungUniversity of Oxford, Jeremy GibbonsUniversity of Oxford, UK
Pre-print
15:30 - 16:30
Meeting
Fixed Points for Markov Decision Processes
PPS
Johannes HölzlTechnische Universität München
Pre-print
15:30 - 16:30
Meeting
A Denotational Semantics of a Probabilistic Stream-Processing Language
PPS
Yohei MiyamotoGraduate School of Informatics, Kyoto University, Kohei Suenaga, Koji NakazawaGraduate School of Information Science, Nagoya University
Pre-print
15:30 - 16:30
Meeting
Observation Propagation for Importance Sampling with Likelihood Weighting
PPS
Ryan CulpepperNortheastern University
Pre-print
15:30 - 16:30
Meeting
Problems of the Lightweight Implementation of Probabilistic Programming
PPS
Pre-print
15:30 - 16:30
Meeting
Parameterized Probability Monad
PPS
Adam ŚcibiorUniversity of Cambridge, Andrew D. GordonMicrosoft Research and University of Edinburgh
Pre-print
15:30 - 16:30
Meeting
Reproducing Kernel Hilbert Space Semantics for Probabilistic Programs
PPS
Adam ŚcibiorUniversity of Cambridge, Bernhard SchölkopfMPI Tuebingen
Pre-print