PROBPROG2021

Wednesday 20 October

ID Floor Poster Type
2 2 Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models [pdf]
George PB Matheos (MIT)*, Alexander K. Lew (MIT), Matin Ghavamizadeh (MIT), Stuart Russell (UC Berkeley), Marco Cusumano-Towner (MIT), Vikash Mansinghka (Massachusetts Institute of Technology) 		Syndicated Submission
5 2 SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
Feras Saad (Massachusetts Institute of Technology)*, Martin Rinard (MIT), Vikash Mansinghka (Massachusetts Institute of Technology) 		Syndicated Submission
7 2 Inference in Network-based Epidemiological Simulations with Probabilistic Programming [pdf]
Niklas Smedemark-Margulies (Northeastern University)*, Robin Walters (Northeastern University), Heiko Zimmermann (Northeastern University), Lucas Laird (MIT Lincoln Laboratory), Neela Kaushik (MIT Lincoln Laboratory), Rajmonda S. Caceres (MIT Lincoln Laboratory), Jan-Willem van de Meent (Northeastern University) 		Extended Abstract
10 2 Few-shot Bayesian inference of 3D objects and scenes via probabilistic programs [pdf]
Nishad Gothoskar (MIT)*, Ben Zinberg (MIT), Marco Cusumano-Towner (MIT), Falk Pollok (IBM Research AI), Joshua Tenenbaum (MIT), Dan Gutfreund (IBM), Vikash Mansinghka (Massachusetts Institute of Technology) 		Extended Abstract
11 2 PPCheck: Verifying the Equivalence of Probabilistic Programs [pdf]
Alexandru Dinu (National University of Singapore)*, Sourav Chakraborty (Indian Statistical Institute), Kuldeep S Meel (National University of Singapore) 		Extended Abstract
12 2 Assessing Inference Quality for Probabilistic Programs using Multivariate Simulation Based Calibration [pdf]
Sharan Yalburgi (BITS Pilani)*, Jameson Quinn (MIT), Veronica Weiner (MIT), Sam A Witty (University of Massachusetts, Amherst), Vikash Mansinghka (Massachusetts Institute of Technology), Cameron Freer (Massachusetts Institute of Technology) 		Extended Abstract
15 2 High-Dimensional Bayesian Workflow in Pyro [pdf]
Fritz H Obermeyer (Broad Institute)* 		Extended Abstract
16 2 Statically Bounded-Memory Delayed Sampling for Probabilistic Streams [pdf]
Eric Atkinson (MIT), Guillaume Baudart (Inria Paris, École normale supérieure - PSL University), Louis Mandel (IBM Research)*, Charles Yuan (MIT), Michael Carbin (MIT) 		Extended Abstract
18 2 Composing Importance Samplers with Lenses [pdf]
Eli Z Sennesh (Northeastern University)*, Sam Stites (Northeastern University), Jan-Willem van de Meent (Northeastern University) 		Extended Abstract
19 2 Nested Variational Inference [pdf]
Heiko Zimmermann (Northeastern University)*, Hao Wu (Northeastern University), Babak Esmaeili (Northeastern University), Sam Stites (Northeastern University), Jan-Willem van de Meent (Northeastern University) 		Syndicated Submission
20 2 Learning Proposals for Probabilistic Programs with Inference Combinators [pdf]
Sam Stites (Northeastern University)*, Heiko Zimmermann (Northeastern University), Hao Wu (Northeastern University), Eli Z Sennesh (Northeastern University), Jan-Willem van de Meent (Northeastern University) 		Syndicated Submission
21 2 Variant Generation for Augmented Gibbs Samplers [pdf]
Sachith H Seneviratne (University of Melbourne)*, Wray Buntine (Monash University) 		Extended Abstract
24 2 Vate: Runtime Adaptable Probabilistic Programming for Java [pdf]
Daniel Goodman (Oracle Labs)*, Adam C Pocock (Oracle Labs), Jason Peck (Oracle Labs), Guy Steele (Oracle Labs) 		Syndicated Submission
26 2 flip-hoisting: A Probabilistic Program Optimization for Exact Inference
Yu-Hsi Cheng (University of California, Los Angeles)*, Steven J Holtzen (Northeastern University), Guy Van den Broeck (UCLA), Todd Millstein (UCLA) 		Extended Abstract
28 2 A Pearl Pearl [pdf]
Nada Amin (Harvard University)*, William E Byrd (University of Alabama at Birmingham), Joseph A Cottam (PNNL), Marc-Antoine Parent (Solutions Conversence inc.), Jeremy Zucker (Pacific Northwest National Laboratory) 		Extended Abstract
29 3 Accelerating inference for InferenceQL via sum-product expressions [pdf]
Ulrich Schaechtle (MIT)*, Zane Shelby (MIT), Cameron Freer (Massachusetts Institute of Technology), Feras Saad (Massachusetts Institute of Technology), Vikash Mansinghka (Massachusetts Institute of Technology) 		Extended Abstract
30 3 Compartmental Models for COVID-19 and Control via Policy Interventions [pdf]
Swapneel S Mehta (New York University)*, Noah S Kasmanoff (New York University) 		Extended Abstract
34 3 Probabilistic Programming for Bond Trading [pdf]
Veronica Weiner (MIT)*, Jameson A Quinn (Jameson Quinn)*, Harish Tella (MIT) and Vikash Mansinghka (MIT) 		Extended Abstract
37 3 Explorations of causal probabilistic programming approaches for rule-based models of biological signaling pathways [pdf]
Devon Kohler (Northeastern University)*, Jeremy Zucker (Pacific Northwest National Laboratory), Vartika Tewari (Northeastern University ), Karen Sachs (Next Generation Analytics), Robert Ness (Altdeep.ai), Olga Vitek (Northeastern University) 		Extended Abstract
39 3 Recursive Monte Carlo and Variational Inference
Alexander K. Lew (MIT)*, Marco Cusumano-Towner (MIT), Vikash Mansinghka (Massachusetts Institute of Technology) 		Extended Abstract
40 3 Causal Probabilistic Programming Without Tears [pdf]
Eli Bingham (Broad Institute of MIT and Harvard)*, James Koppel (MIT), Alexander K. Lew (MIT), Robert Ness (Altdeep), Zenna Tavares (MIT), Sam A Witty (University of Massachusetts, Amherst), Jeremy Zucker (Pacific Northwest National Laboratory) 		Extended Abstract
41 3 Propagating Gradients through Weights in Particle Filters [pdf]
Adam Scibior (University of British Columbia)*, Vaden W Masrani (University of British Columbia), Frank Wood (University of British Columbia) 		Extended Abstract

Thursday 21 October

ID Floor Poster Type
1 2 Complex Coordinate-Based Meta-Analysis with Probabilistic Programming
Valentin Iovene (Inria)*, Gaston E Zanitti (Inria), Demian Wassermann (Inria) 		Syndicated Submission
3 2 Automated Termination Analysis of Polynomial Probabilistic Programs [pdf]
Marcel Moosbrugger (TU Wien)*, Ezio Bartocci (TU Wien), Joost-Pieter Katoen (RWTH Aachen University), Laura Kovaćs (TU Wien) 		Syndicated Submission
4 2 Probabilistic inductive constraint logic [pdf]
Fabrizio Riguzzi (Universita di Ferrara), Elena Bellodi (University of Ferrara)*, Riccardo Zese (University of Ferrara), Marco Alberti (University of Ferrara), Evelina Lamma (University of Ferrara) 		Syndicated Submission
6 2 GPflux: A Library for Deep Gaussian Processes [pdf]
Vincent Dutordoir (University of Cambridge)*, Eric L L Hambro (Facebook AI Research), Hugh Salimbeni (G-Research), John McLeod (Secondmind.ai), Felix Leibfried (PROWLER.io), Artem Artemev (Imperial College London), Mark van der Wilk (Imperial College London), James Hensman (PROWLER.io), Marc Deisenroth (University College London), ST John (Secondmind.ai) 		Extended Abstract
8 2 How to train your program: a probabilistic programming pattern for Bayesian learning from data [pdf]
David Tolpin (Ben Gurion University of the Negev & PUB+)* 		Extended Abstract
13 2 From Probabilistic NetKAT to ProbLog: New Algorithms for Inference and Learning in Probabilistic Networks [pdf]
Birthe Van den Berg (KU Leuven), Timothy van Bremen (KU Leuven)*, Vincent Derkinderen (KU Leuven), Angelika Kimmig (KU Leuven), Tom Schrijvers (KU Leuven), Luc De Raedt (KU Leuven) 		Extended Abstract
14 2 Automatic Guide Generation for Stan via NumPyro [pdf]
Guillaume Baudart (Inria Paris, École normale supérieure - PSL University)*, Louis Mandel (IBM Research) 		Extended Abstract
17 2 Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect [pdf]
Alessio Benavoli (Trinity College Dublin)*, Jason Wyse (Trinity College Dublin), Arthur White (Trinity College Dublin) 		Extended Abstract
22 2 A Semantics for Hybrid Probabilistic Logic Programs with Function Symbols [pdf]
Damiano Azzolini (University of Ferrara)*, Fabrizio Riguzzi (Universita di Ferrara), Evelina Lamma (University of Ferrara) 		Syndicated Submission
23 2 Compositional Semantics for Probabilistic Programs with Exact Conditioning [pdf]
Dario M Stein (University of Oxford)*, Sam Staton (University of Oxford) 		Syndicated Submission
27 2 Probabilistic Programs with Stochastic Conditioning (ICML 2021) [pdf]
David Tolpin (Ben Gurion University of the Negev & PUB+)*, Yuan Zhou (University of Oxford), Tom Rainforth (University of Oxford), Hongseok Yang (KAIST) 		Syndicated Submission
31 2 Efficient Generative Modelling of Protein StructureFragments using a Deep Markov Model [pdf]
Christian B Thygesen (University of Copenhagen / Evaxion Biotech)*, Ahmad Salim Al-Sibahi (University of Copenhagen), Christian Skjødt Steenmans (Evaxion Biotech), Lys Sanz Moreta (University of Copenhagen), Anders Bundgård Sørensen (Evaxion Biotech), Thomas Hamelryck (University of Copenhagen) 		Syndicated Submission
32 2 Expectation Programming [pdf]
Tim Reichelt (University of Oxford)*, Adam Golinski (University of Oxford), Luke Ong (University of Oxford), Tom Rainforth (University of Oxford) 		Extended Abstract
33 2 Nonparametric Hamiltonian Monte Carlo [pdf]
Carol Mak (University of Oxford)*, Fabian Zaiser (University of Oxford), Luke Ong (University of Oxford) 		Syndicated Submission
35 2 Variational Energy Conserving Subsampling [pdf]
Ola Rønning (University of Copenhagen)*, Thomas Hamelryck (University of Copenhagen) 		Extended Abstract
36 2 Autocorrect for Probabilistic Models
Robert Zinkov (University of Oxford)* 		Extended Abstract
42 3 A graphical-model semantics for probabilistic programs [pdf]
Hugo Paquet (University of Oxford)* 		Syndicated Submission
43 3 LazyPPL: Sampling-by-need in non-parametric probabilistic programs [pdf]
Sam Staton (University of Oxford)* 		Extended Abstract
44 3 Parallel Variable Elimination in Dynamic Factor Graphs
Eli Bingham (Broad Institute of MIT and Harvard)*, Fritz Obermeyer (), Yerdos Ordabayev (Brandeis University), Du Phan (University of Illinois at Urbana-Champaign), Martin Jankowiak () 		Extended Abstract

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