Using probabilistic programs as proposals ( PPS 2018 - Probabilistic Programming Languages, Semantics, and Systems (PPS 2018)) - POPL 2018

Sun 7 - Sat 13 January 2018 Los Angeles, California, United States

Who

Marco Cusumano-Towner, Vikash Mansinghka

Track

Time Zone

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When

Tue 9 Jan 2018 10:17 - 10:19 at Bradbury - POSTER SESSION (14 posters - not talks)

Abstract

blah blah blah

Marco Cusumano-Towner

MIT-CSAIL

Vikash Mansinghka

Massachusetts Institute of Technology

Time Zone

The program is currently displayed in (GMT-08:00) Tijuana, Baja California.

Use conference time zone: (GMT-08:00) Tijuana, Baja CaliforniaSelect other time zone

The GMT offsets shown reflect the offsets at the moment of the conference.

Time Band

By setting a time band, the program will dim events that are outside this time window. This is useful for (virtual) conferences with a continuous program (with repeated sessions).
The time band will also limit the events that are included in the personal iCalendar subscription service.

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Session Program

Tue 9 Jan
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	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