Write a Blog >>
NetPL 2018
Mon 8 - Sat 13 January 2018 Los Angeles, California, United States
co-located with POPL 2018
POPL 2018 (series) / NetPL 2018 (series) / Omni Hotel /

Room information: Bradbury

VenueOmni Hotel
Room nameBradbury
Floor0
Room InformationNo extra information available
Program

You're viewing the program in a time zone which is different from your device's time zone change time zone

Mon 8 Jan

Displayed time zone: Tijuana, Baja California change

09:00 - 10:30
Equational reasoning for probabilistic programmingPOPL TutorialFest at Bradbury
09:00
90m
Talk
Equational reasoning for probabilistic programming.
POPL TutorialFest
A: Chung-chieh Shan Indiana University, USA
Media Attached
11:00 - 12:00
Equational reasoning for probabilistic programmingPOPL TutorialFest at Bradbury
11:00
60m
Talk
Equational reasoning for probabilistic programming.
POPL TutorialFest
A: Chung-chieh Shan Indiana University, USA
Media Attached
14:00 - 15:30
Computational Higher Type TheoryPOPL TutorialFest at Bradbury
14:00
90m
Talk
Computational Higher Type Theory
POPL TutorialFest
A: Robert Harper Carnegie Mellon University, A: Carlo Angiuli Carnegie Mellon University
Media Attached
16:00 - 17:00
Computational Higher Type TheoryPOPL TutorialFest at Bradbury
16:00
60m
Talk
Computational Higher Type Theory
POPL TutorialFest
A: Robert Harper Carnegie Mellon University, A: Carlo Angiuli Carnegie Mellon University
Media Attached

Tue 9 Jan

Displayed time zone: Tijuana, Baja California change

09:00 - 10:00
SESSION I (invited talk) PPS at Bradbury
Chair(s): Andrew D. Gordon Microsoft Research and University of Edinburgh
09:00
60m
Talk
Software is eating the world, but ML is going to eat software
PPS
Erik Meijer
10:00 - 10:30
POSTER SESSION (14 posters - not talks) PPS at Bradbury
10:00
2m
Talk
Probabilistic Programming for Robotics
PPS
Nils Napp SUNY at Buffalo, Marco Gaboardi University at Buffalo, SUNY
10:02
2m
Talk
Game Semantics for Probabilistic Programs
PPS
C.-H. Luke Ong University of Oxford, Matthijs Vákár University of Oxford
10:04
2m
Talk
Interactive Writing and Debugging of Bayesian Probabilistic Programs
PPS
Javier Burroni , Arjun Guha University of Massachusetts, Amherst, David Jensen University of Massachusetts Amherst
Pre-print
10:06
2m
Talk
Deep Amortized Inference for Probabilistic Programs using Adversarial Compilation
PPS
Mahdi Azarafrooz Cylance
10:08
2m
Talk
Comparing the speed of probabilistic processes
PPS
Mathias Ruggaard Pedersen Aalborg University, Nathanaël Fijalkow Alan Turing Institute, Giorgio Bacci Aalborg University, Kim Larsen Aalborg University, Radu Mardare Aalborg University
10:10
2m
Talk
Using Reinforcement Learning for Probabilistic Program Inference
PPS
Avi Pfeffer Charles River Analytics
10:12
2m
Talk
TensorFlow Distributions
PPS
Joshua V. Dillon Google, Ian Langmore Google, Eugene Brevdo Google, Matt Hoffman Google, Dave Moore Google, Brian Patton Google, Rif A. Saurous Google, Dustin Tran , Srinivas Vasudevan Google
Link to publication Pre-print
10:15
2m
Talk
Constructive probabilistic semantics with non-spatial locales
PPS
Benjamin Sherman Massachusetts Institute of Technology, USA, Jared Tramontano Massachusetts Institute of Technology, Michael Carbin MIT
Pre-print
10:17
2m
Talk
Using probabilistic programs as proposals
PPS
Marco Cusumano-Towner MIT-CSAIL, Vikash Mansinghka Massachusetts Institute of Technology
10:19
2m
Talk
Probabilistic Program Equivalence for NetKAT
PPS
Steffen Smolka Cornell University, David Kahn Cornell University, Praveen Kumar Cornell University, Nate Foster Cornell University, Dexter Kozen , Alexandra Silva University College London
Link to publication File Attached
10:21
2m
Talk
Reasoning about Divergences via Span-liftings
PPS
Tetsuya Sato University at Buffalo, SUNY, USA
10:23
2m
Talk
Probabilistic Models for Assured Position, Navigation and Timing
PPS
Andres Molina-Markham The MITRE Corporation
10:25
2m
Talk
The Support Method of Computing Expectations
PPS
Avi Pfeffer Charles River Analytics
10:27
2m
Talk
Combining static and dynamic optimizations using closed-form solutions
PPS
Daniel Lundén KTH Royal Institute of Technology, David Broman KTH Royal Institute of Technology, Lawrence M. Murray Uppsala University
10:30 - 12:00
SESSION II (3 talks) PPS at Bradbury
Chair(s): Erik Meijer
10:30
30m
Talk
The semantic structure of quasi-Borel spaces: algebra, logic, and recursion
PPS
Chris Heunen University of Edinburgh, Ohad Kammar University of Oxford, Sean Moss University of Oxford, Adam Ścibior University of Cambridge and MPI Tuebingen, Sam Staton University of Oxford, Matthijs Vákár University of Oxford, Hongseok Yang University of Oxford
11:00
30m
Talk
Stable, measurable functions and probabilistic programs
PPS
Michele Pagani University Paris Diderot, Thomas Ehrhard CNRS and University Paris Diderot, Christine Tasson University Paris Diderot
11:30
30m
Talk
Formal Methods For Probabilistic Programming
PPS
Daniel Selsam Stanford University
14:00 - 15:30
SESSION III (invited tutorial + 1 talk) PPS at Bradbury
Chair(s): Cameron Freer Remine and Borelian
14:00
60m
Talk
Deep Probabilistic Programming: TensorFlow Distributions and Edward
PPS
Rif A. Saurous Google, Dustin Tran
15:00
30m
Talk
More support for symbolic disintegration
PPS
Praveen Narayanan Indiana University, USA, Chung-chieh Shan Indiana University, USA
16:00 - 18:00
SESSION IV (4 talks) PPS at Bradbury
Chair(s): Rif A. Saurous Google
16:00
30m
Talk
Auxiliary variables in Probabilistic Programs
PPS
Ekansh Sharma , Daniel Roy
16:30
30m
Talk
Probabilistic Program Inference With Abstractions
PPS
Steven Holtzen University of California, Los Angeles, Guy Van den Broeck University of California, Los Angeles, Todd Millstein University of California, Los Angeles
Pre-print
17:00
30m
Talk
SlicStan: Improving Probabilistic Programming using Information Flow Analysis
PPS
Maria I. Gorinova The University of Edinburgh, Andrew D. Gordon Microsoft Research and University of Edinburgh, Charles Sutton University of Edinburgh
Pre-print
17:30
30m
Talk
Contextual Equivalence for a Probabilistic Language with Continuous Random Variables and Recursion
PPS
Mitchell Wand Northeastern University, USA, Theophilos Giannakopoulos BAE Systems, Inc., Andrew Cobb Northeastern University, Ryan Culpepper Northeastern University

Mon 8 Jan

Displayed time zone: Tijuana, Baja California change

Room9:003010:003011:003012:003013:003014:003015:003016:0030
Bradbury

POPL TutorialFest

Equational reasoning for probabilistic programming

POPL TutorialFest

Equational reasoning for probabilistic programming

POPL TutorialFest

Computational Higher Type Theory

POPL TutorialFest

Computational Higher Type Theory

Tue 9 Jan

Displayed time zone: Tijuana, Baja California change

Room9:003010:003011:003012:003013:003014:003015:003016:003017:0030
Bradbury

PPS

SESSION I (invited talk)

PPS

POSTER SESSION (14 posters - not talks)

PPS

SESSION II (3 talks)

PPS

SESSION III (invited tutorial + 1 talk)

PPS

SESSION IV (4 talks)

Mon 8 Jan

Displayed time zone: Tijuana, Baja California change

Room9:0015304510:0015304511:0015304512:0015304513:0015304514:0015304515:0015304516:00153045
Bradbury
POPL TutorialFest
Equational reasoning for probabilistic programming.
09:00 - 10:30
POPL TutorialFest
Equational reasoning for probabilistic programming.
11:00 - 12:00
POPL TutorialFest
Computational Higher Type Theory
14:00 - 15:30
POPL TutorialFest
Computational Higher Type Theory
16:00 - 17:00

Tue 9 Jan

Displayed time zone: Tijuana, Baja California change

Room9:0015304510:0015304511:0015304512:0015304513:0015304514:0015304515:0015304516:0015304517:00153045
Bradbury
PPS
Software is eating the world, but ML is going to eat software
09:00 - 10:00
PPS
Probabilistic Programming for Robotics
10:00 - 10:02
PPS
Game Semantics for Probabilistic Programs
10:02 - 10:04
PPS
Interactive Writing and Debugging of Bayesian Probabilistic Programs
10:04 - 10:06
PPS
Deep Amortized Inference for Probabilistic Programs using Adversarial C ...
10:06 - 10:08
PPS
Comparing the speed of probabilistic processes
10:08 - 10:10
PPS
Using Reinforcement Learning for Probabilistic Program Inference
10:10 - 10:12
PPS
TensorFlow Distributions
10:12 - 10:15
PPS
Constructive probabilistic semantics with non-spatial locales
10:15 - 10:17
PPS
Using probabilistic programs as proposals
10:17 - 10:19
PPS
Probabilistic Program Equivalence for NetKAT
10:19 - 10:21
PPS
Reasoning about Divergences via Span-liftings
10:21 - 10:23
PPS
Probabilistic Models for Assured Position, Navigation and Timing
10:23 - 10:25
PPS
The Support Method of Computing Expectations
10:25 - 10:27
PPS
Combining static and dynamic optimizations using closed-form solutions
10:27 - 10:30
PPS
The semantic structure of quasi-Borel spaces: algebra, logic, and recursion
10:30 - 11:00
PPS
Stable, measurable functions and probabilistic programs
11:00 - 11:30
PPS
Formal Methods For Probabilistic Programming
11:30 - 12:00
PPS
Deep Probabilistic Programming: TensorFlow Distributions and Edward
14:00 - 15:00
PPS
More support for symbolic disintegration
15:00 - 15:30
PPS
Auxiliary variables in Probabilistic Programs
16:00 - 16:30
PPS
Probabilistic Program Inference With Abstractions
16:30 - 17:00
PPS
SlicStan: Improving Probabilistic Programming using Information Flow An ...
17:00 - 17:30
PPS
Contextual Equivalence for a Probabilistic Language with Continuous Ran ...
17:30 - 18:00