Write a Blog >>
POPL 2021
Sun 17 - Fri 22 January 2021 Online
Sun 17 Jan 2021 19:17 - 19:30 at LAFI - Session 2 Chair(s): Dougal Maclaurin

The Statues algorithm is a new probabilistic inference algorithm that gives exact results in the scope of discrete random variables. This algorithm calculates the marginal probability distributions on graphical models defined as directed acyclic graphs. These models are made up of five primitives that allow expressing, in particular, conditioning, joint probability distributions, Bayesian networks, discrete Markov chains and probabilistic arithmetic. The Statues algorithm relies on an original technique based on the generator construct, a special form of coroutine. This new algorithm aims to promote both efficiency and scope of application. This makes it valuable regarding other probabilistic inference approaches, especially in the field of probabilistic programming.

Statues algorithm - short presentation for LAFI21 (statues_algorithm_lafi21-denis.pptx)913KiB
Statues algorithm - long presentation (statues_algorithm.pptx)1.18MiB

Sun 17 Jan
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

18:00 - 19:30: Session 2LAFI at LAFI
Chair(s): Dougal MaclaurinGoogle Research
18:00 - 18:12
Talk
Enzyme: High-Performance Automatic Differentiation of LLVM
LAFI
William S. MosesMassachusetts Institute of Technology, Valentin ChuravyMIT CSAIL
18:12 - 18:25
Talk
Parametric Inversion of Non-Invertible Programs
LAFI
Zenna TavaresMassachusetts Institute of Technology, Javier BurroniUniversity of Massachusetts Amherst, Edgar MinasyanPrinceton University, David MorejonMassachusetts Institute of Technology, Armando Solar-LezamaMassachusetts Institute of Technology
File Attached
18:25 - 18:38
Talk
Bayesian Neural Ordinary Differential Equations
LAFI
Raj DandekarMIT, Vaibhav DixitJulia Computing, Mohamed TarekUNSW Canberra, Australia, Aslan Garcia ValadezNational Autonomous University of Mexico, Chris RackauckasMIT
Pre-print Media Attached File Attached
18:38 - 18:51
Talk
On the Automatic Derivation of Importance Samplers from Pairs of Probabilistic Programs
LAFI
Alexander K. LewMassachusetts Institute of Technology, USA, Ben Sherman, Marco Cusumano-TownerMIT-CSAIL, Michael CarbinMassachusetts Institute of Technology, Vikash MansinghkaMIT
Media Attached
18:51 - 19:04
Talk
Decomposing reverse-mode automatic differentiation
LAFI
Roy FrostigGoogle Research, Matthew JohnsonGoogle Brain, Dougal MaclaurinGoogle Research, Adam PaszkeGoogle Research, Alexey RadulGoogle Research
File Attached
19:04 - 19:17
Talk
Genify.jl: Transforming Julia into Gen to enable programmable inference
LAFI
Tan Zhi-XuanMassachusetts Institute of Technology, McCoy R. BeckerCharles River Analytics, Vikash MansinghkaMIT
Media Attached File Attached
19:17 - 19:30
Talk
Probabilistic Inference Using Generators: the Statues Algorithm
LAFI
Pierre Denisindependent scholar
Link to publication DOI Pre-print Media Attached File Attached