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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

Displayed time zone: Guadalajara, Mexico City, Monterrey change

15:30 - 16:30
Poster SessionPPS at Room St Petersburg II
15:30
60m
Meeting
Insomnia: Types and Modules for Probabilistic Programming
PPS
Aleksey Kliger Xamarin, Inc., Sean Stromsten BAE Systems, Inc.
Pre-print
15:30
60m
Meeting
Finite-depth Higher-order Abstract Syntax Trees for Reasoning about Probabilistic Programs
PPS
Theophilos Giannakopoulos BAE Systems, Inc., Mitchell Wand Northeastern University, Andrew Cobb Northeastern University
Pre-print
15:30
60m
Meeting
Coalgebraic Trace Semantics for Probabilistic Processes: Preliminary Proposal
PPS
Larry Moss Indiana University, Chung-chieh Shan Indiana University, Alexandra Silva Radboud University Nijmegen
Pre-print
15:30
60m
Meeting
Reasoning about Probability and Nondeterminism
PPS
Faris Abou-Saleh University of Oxford, Kwok-Ho Cheung University of Oxford, Jeremy Gibbons University of Oxford, UK
Pre-print
15:30
60m
Meeting
Fixed Points for Markov Decision Processes
PPS
Johannes Hölzl Technische Universität München
Pre-print
15:30
60m
Meeting
A Denotational Semantics of a Probabilistic Stream-Processing Language
PPS
Yohei Miyamoto Graduate School of Informatics, Kyoto University, Kohei Suenaga , Koji Nakazawa Graduate School of Information Science, Nagoya University
Pre-print
15:30
60m
Meeting
Observation Propagation for Importance Sampling with Likelihood Weighting
PPS
Ryan Culpepper Northeastern University
Pre-print
15:30
60m
Meeting
Problems of the Lightweight Implementation of Probabilistic Programming
PPS
Pre-print
15:30
60m
Meeting
Parameterized Probability Monad
PPS
Adam Ścibior University of Cambridge, Andrew D. Gordon Microsoft Research and University of Edinburgh
Pre-print
15:30
60m
Meeting
Reproducing Kernel Hilbert Space Semantics for Probabilistic Programs
PPS
Adam Ścibior University of Cambridge, Bernhard Schölkopf MPI Tuebingen
Pre-print