PROBPROG 2018

The International Conference on Probabilistic Programming

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Friday October 5th, 12:00-15:00

ID Track Poster
3 Artificial Intelligence Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu (UC Berkeley)*, Siddharth Srivastava (Arizona State University), Nicholas Hay , Simon S Du (Carnegie Mellon University), Stuart Russell (UC Berkeley)
4 Artificial Intelligence Meta-Learning MCMC Proposals
Tongzhou Wang (Facebook AI Research), Yi Wu (UC Berkeley)*, David Moore (University of California, Berk), Stuart Russell (UC Berkeley)
22 Artificial Intelligence Dual Probabilistic Programming
Alessio Benavoli (IDSIA)*
42 Artificial Intelligence Unsupervised structure learning for graph-based probabilistic programs
Matthew C Overlan (University of Rochester)*, Robert Jacobs (University of Rochester)
60 Artificial Intelligence Formalizing People's Intuitive Theory of Emotions as a Probabilistic Program
Sean Houlihan (MIT)*, Max Kleiman-Weiner (MIT), Joshua Tenenbaum (MIT), Rebecca Saxe (MIT)
74 Artificial Intelligence Inference Over Programs That Make Predictions
Yura Perov (Babylon Health)*
84 Artificial Intelligence Neural Theorem Proving on Natural Language
Matko Bosnjak (UCL)*, Pasquale Minervini (University College London), Andres Campero (MIT), Tim Rocktaschel (Oxford), Edward Grefenstette (DeepMind), Sebastian Riedel (UCL)
8 Languages and Systems Towards Lambda Abstractions for Probabilistic NetKAT
Alexander Vandenbroucke (KU Leuven)*, Tom Schrijvers (KU Leuven)
18 Languages and Systems Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Feras Saad (MIT)*
19 Languages and Systems ArviZ: a unified library for Bayesian model criticism and visualization in Python
Colin Carroll (Freebird, Inc)*, Austin Rochford (Monetate, Inc)
20 Languages and Systems Human-in-the-loop Learning for Probabilistic Programming
Sriraam Natarajan (UT Dallas), Phillip Odom (Georgia Insitute of Technology)*, Tushar Khot (Allen Institute for AI), Kristian Kersting (TU Darmstadt), Jude Shavlik (UW Madison)
28 Languages and Systems The cplint Probabilistic Logic Programming System
Fabrizio Riguzzi (Universita di Ferrara)*, Marco Alberti (University of Ferrara), Elena Bellodi (University of Ferrara), Giuseppe Cota (University of Ferrara), Evelina Lamma (University of Ferrara), Riccardo Zese (University of Ferrara)
30 Languages and Systems Probabilistic Programming with Gaussian Processes
William Tebbutt (University of Cambridge)*, Wessel P Bruinsma (University of Cambridge), Richard Turner (University of Cambridge)
32 Languages and Systems Probabilistic Reactive Programming
Louis Mandel (IBM Research)*, Guillaume Baudart (IBM Research), Avraham Shinnar (IBM Research), Kiran Kate (IBM Research), Martin Hirzel (IBM Research)
36 Languages and Systems Watertight Probabilistic Abstractions in Python
Guillaume Baudart (IBM Research), Avraham Shinnar (IBM Research), Martin Hirzel (IBM Research)*, Louis Mandel (IBM Research)
40 Languages and Systems Spreadsheet Probabilistic Programming
William R Smith (Invrea)*, Mike H Wu (Stanford University), Hongseok Yang , Frank Wood (University of British Columbia)
44 Languages and Systems Combinators for Modeling and Inference
Eli Z Sennesh (Northeastern University)*, Hao Wu (Northeastern University), Jan-Willem van de Meent (Northeastern University)
46 Languages and Systems An Extensible Architecture for Fast Programmable Inference in Probabilistic Programs
Marco Cusumano-Towner (MIT)*
56 Languages and Systems Leveraging conditional independence in Pyro
Fritz H Obermeyer (Uber AI Labs)*, Eli Bingham (Uber AI Labs), Martin Jankowiak (Uber AI Labs), Neeraj Pradhan (Uber AI Labs), Noah Goodman (Uber AI Labs)
62 Languages and Systems Effect Handling for Composable Program Transformations in Edward2
Dave Moore (Google)*, Maria I Gorinova (Google)
64 Languages and Systems Sensitivity Analysis for Probabilistic Programs with PSense
Zixin Huang (UIUC), Zhenbang Wang (UIUC), Sasa Misailovic (UIUC)*
66 Languages and Systems Probabilistic programming for data-efficient robotics
Alexander Lavin (Vicarious)*, Vikash Mansinghka (MIT)
70 Languages and Systems ProbFuzz: Fuzzing Probabilistic Programming Systems
Saikat Dutta (UIUC)*, Owolabi Legunse (UIUC), Zixin Huang (UIUC), Sasa Misailovic (UIUC)
6 Practice Generation of BUGS
Andrew - Thomas (MRC)*
14 Practice Reactive Probabilistic Programming
Pedro Zuidberg Dos Martires (KU Leuven)*, Sebastijan Dumancic (KU LEUVEN)
24 Practice Forecasting a Stock's Remaining Intraday Volume
Alex Constandache (Thomson Reuters), Omar Bari (Thomson Reuters)*
26 Practice A small program can be a big challenge
David Tolpin (Offtopia)*
45 Practice Detecting multivariate relationships in multivariate data by synthesizing and analyzing probabilistic programs
Ulrich Schaechtle (MIT)*
49 Practice Synthesizing probabilistic programs to predict empirical protein stability from Rosetta simulations
Ulrich Schaechtle (MIT)*
54 Practice GATK gCNV: accurate germline copy-number variant discovery from sequencing read-depth data
Mehrtash Babadi (Broad Institute)*, Samuel Lee (Broad Institute), Andrey Smirnov (Broad Institute)
72 Practice Probabilistic Programming for Voucher Information Extraction
Ahmad Salim Al-Sibahi (University of Copenhagen/Skanned)*, Thomas Hamelryck (University of Copenhagen), Fritz Henglein (University of Copenhagen)
76 Practice Probabilistic programming in production with Infer.NET
Yordan Zaykov (Microsoft Research)*, Tom Minka (Microsoft), John Winn (Microsoft Research), John P Guiver (Microsoft Research)
80 Practice BayesDB: probabilistic programming with an SQL-like interface and built-in probabilistic program synthesis
Vikash Mansinghka (MIT)*
5 Statistics Model evaluation should be a first-class citizen in probabilistic programming
Alp Kucukelbir (Fero Labs / Columbia University)*, Yixin Wang (Columbia University), Dustin Tran (Columbia University), David Blei (Columbia University)
10 Statistics Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb (Oxford)*, Adam Golinski (University of Oxford), Rob Zinkov (University of Oxford), N Siddharth (Unversity of Oxford), Tom Rainforth (University of Oxford), Yee Whye Teh (University of Oxford), Frank Wood (University of British Columbia)
16 Statistics Estimating mutual information using a mixture of Dirichlet
Giorgio Corani (IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale))*, Laura Azzimonti (IDSIA), Marco Zaffalon (Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale)
38 Statistics Deep Probabilistic Programs for Causal Survival Analysis
Chris Hart (Babylon Health)*
58 Statistics Causal Graphs vs. Causal Programs: The Example of Conditional Branching
Sam A Witty (University of Massachusetts, Amherst)*, David Jensen (University of Massachusetts Amherst)
68 Statistics Exact Quantification of Continuity Correction Error in Probabilistic Programs
Jacob Laurel (University of Illinois Urbana Champaign)*
82 Statistics The Random Conditional Distribution for Higher-Order Probabilistic Inference
Zenna Tavares (MIT)*, Xin Zhang (MIT), Javier Burroni (UMass Amherst), Edgar Minasyan (MIT), Rajesh Ranganath (New York University), Armando Solar-Lezama (MIT)
59 Practice Variable Elimination with Automatic Differentiation (VEAD) for Model Sensitivity Analysis
Jeff Druce (Charles River Analytics)*

Saturday October 6th, 12:00-14:40

ID Track Poster
31 Artificial Intelligence Auditory scene analysis as neurally-guided inference in a probabilistic program
Luke Hewitt (MIT)*, Maddie Cusimano (MIT)
35 Artificial Intelligence Approximate Counting for Fast Inference and Learning in Probabilistic Programming
Mayukh Das (UT Dallas)*, Devendra Singh Dhami (UT Dallas), Kunapulli Gautam (U. Dallas), Kristian Kersting (TU Darmstadt), Sriraam Natarajan (UT Dallas)
37 Artificial Intelligence SP3 – Sum Product Probabilistic Programming
Alejandro Molina (TU Darmstadt), Karl Stelzner (TU Darmstadt), Robert Peharz (University of Cambridge), Antonio Vergari (MPI for Intelligent Systems), Martin Trapp (Austrian Research Institute for Artificial Intelligence), Isabel Valera (MPI for Intelligent Systems), Zoubin Ghahramani (University of Cambridge), Kristian Kersting (TU Darmstadt)*
7 Languages and Systems ForneyLab.jl: Fast and flexible automated inference through message passing in Julia
Marco Cox (Eindhoven University of Technology)*, Thijs van de Laar (Eindhoven University of Technology), Bert de Vries (Eindhoven University of Technology)
13 Languages and Systems Automatic Discovery of Static Structures in Probabilistic Programs
Daniel Lundén (KTH Royal Institute of Technology)*, David Broman (KTH Royal Institute of Technology), Fredrik Ronquist (Swedish Museum of Natural History), Lawrence M. Murray (Uppsala University)
21 Languages and Systems A Program Analysis Perspective on Expected Sampling Times
Kevin Batz (RWTH Aachen University), Benjamin Lucien Kaminski (RWTH Aachen University), Joost-Pieter Katoen (RWTH Aachen University), Christoph Matheja (RWTH Aachen University)*
25 Languages and Systems Automatically Batching the No U-Turn Sampler
Alexey Radul (Google)*, Brian Patton (Google Inc.), Dougal Maclaurin (Google Inc.), DeLesley Hutchins (Google Inc.)
27 Languages and Systems InferPy: An Easy-to-use Probabilistic Programming Language over TensorFlow
Rafael Cabañas de Paz (University of Almería)*, Andres Masegosa (University of Almeria), Antonio Salmeron (Universidad de Almeria)
39 Languages and Systems Grappa: An Extensible PPL Compiler Framework
Eddy Westbrook (Galois, Inc)*
43 Languages and Systems Debugging Probabilistic Programs: Lessons from Debugging Research
Henry Lieberman (MIT)*, Yen-Ling Kuo (MIT), Valeria Staneva (MIT)
51 Languages and Systems Automated enumeration of discrete latent variables
Fritz H Obermeyer (Uber AI Labs)*, Eli Bingham (Uber AI Labs), Martin Jankowiak (Uber AI Labs), Neeraj Pradhan (Uber AI Labs), Noah Goodman (Uber AI Labs)
57 Languages and Systems Towards closed-loop crowdsourcing and human computation
Jordan W Suchow (UC Berkeley)*
63 Languages and Systems Transpiling Stan models to Pyro
Jonathan P. Chen (Uber AI Labs)*, Eli Bingham (Uber AI Labs), Noah Goodman (Uber AI Labs), Rohit Singh (Uber AI Labs & MIT)
69 Languages and Systems Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D Hoffman (Google)*, Matthew J Johnson (Google Brain), Dustin Tran (Google)
71 Languages and Systems Omega: Probabilistic Programming with Predicates
Zenna Tavares (MIT)*, Javier Burroni (UMass Amherst), Edgar Minasyan (MIT), Rajesh Ranganath (New York University), Armando Solar-Lezama (MIT)
73 Languages and Systems Information-relational semantics of the Fifth system
Anthony C Di Franco (University of California, Davis)*
75 Languages and Systems Foundations of ProbProg
Ohad Kammar (University of Oxford), Sam Staton (University of Oxford), Matthijs Vakar (University of Oxford)*
77 Languages and Systems Metaprob: a minimal language for probabilistic metaprogramming
Vikash Mansinghka (MIT)*
83 Languages and Systems Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Yuan Zhou (University of Oxford)*, Bradley J Gram-Hansen (University of Oxford), Kohn Tobias (University of Cambridge), Hongseok Yang (KAIST), Frank Wood (University of British Columbia)
9 Practice Uncertain from sensors to actuators
Vincent Aravantinos (fortiss GmbH)*
15 Practice Custom PyMC3 nonparametric Bayesian models built on top of the scikit-learn API
Daniel Emaasit (Haystax Technology)*, David Jones (Haystax Technology)
17 Practice Probabilistic Search for Structured Data in BayesDB
Feras Saad (MIT)*
47 Practice Probabilistic programs for automated screening of synthetic biology logic circuits
Ulrich Schaechtle (MIT)*
61 Practice Counterfactual Reasoning with Probabilistic Programming
Robert Ness (Gamalon, Inc)*
67 Practice Sequential Bayesian Design of Experiments via Probabilistic Programming
Willie Neiswanger (Carnegie Mellon University)*
23 Statistics Probabilistic programming allows for automated inference in factor graph models
Matteo Scandella (University of Bergamo)*, Lawrence Murray (Uppsala), Thomas Schön (Uppsala University)
29 Statistics Amortized Monte Carlo Integration
Adam Golinski (University of Oxford)*, Yee Whye Teh (University of Oxford), Frank Wood (University of British Columbia), Tom Rainforth (University of Oxford)
41 Statistics Inference for mixture of finite mixture models using the Turing probabilistic programming language
Aled Vaghela (University of Cambridge), Maria Lomeli (Babylon Health)*, Zoubin Ghahramani (University of Cambridge)
53 Statistics Probabilistic programming with custom reversible jump and auxiliary-variable samplers
Marco Cusumano-Towner (MIT)*
65 Statistics Low Communication Distributed Black Box VI
Willie Neiswanger (Carnegie Mellon University)*

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