Write a Blog >>
POPL 2021
Sun 17 - Fri 22 January 2021 Online

Inference concerns re-calibrating program parameters based on observed data, and has gained wide traction in machine learning and data science. Inference can be driven by probabilistic analysis and simulation, and through back-propagation and differentiation. Languages for inference offer built-in support for expressing probabilistic models and inference methods as programs, to ease reasoning, use, and reuse. The recent rise of practical implementations as well as research activity in inference-based programming has renewed the need for semantics to help us share insights and innovations.

This workshop aims to bring programming-language and machine-learning researchers together to advance all aspects of languages for inference. Topics include but are not limited to:

design of programming languages for inference and/or differentiable programming; inference algorithms for probabilistic programming languages, including ones that incorporate automatic differentiation; automatic differentiation algorithms for differentiable programming languages; probabilistic generative modelling and inference; variational and differential modelling and inference; semantics (axiomatic, operational, denotational, games, etc) and types for inference and/or differentiable programming; efficient and correct implementation; and last but not least, applications of inference and/or differentiable programming.

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

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

15:30 - 16:00
15:30
30m
Social Event
Sunday Breakfast Tables
Workshops and Co-located Events
16:00 - 16:50
Invited talk: Vikash K. MansinghkaLAFI at LAFI
Chair(s): Jean-Baptiste TristanBoston College
16:50 - 17:30
Session 1LAFI at LAFI
Chair(s): Jean-Baptiste TristanBoston College
16:50
13m
Talk
Binary Tree Hamiltonian Monte Carlo
LAFI
Carol MakUniversity of Oxford, Fabian ZaiserUniversity of Oxford, C.-H. Luke OngUniversity of Oxford
File Attached
17:03
13m
Talk
An algebraic theory of conditioning
LAFI
Dario SteinUniversity of Oxford, Sam StatonUniversity of Oxford
File Attached
17:16
13m
Talk
The Selection Monad and Decision-Making Languages
LAFI
File Attached
17:30 - 18:00
17:30
30m
Break
Sunday Coffee Break
Workshops and Co-located Events
18:00 - 19:30
Session 2LAFI at LAFI
Chair(s): Dougal MaclaurinGoogle Research
18:00
12m
Talk
Enzyme: High-Performance Automatic Differentiation of LLVM
LAFI
William S. MosesMassachusetts Institute of Technology, Valentin ChuravyMIT CSAIL
18:12
12m
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
12m
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
12m
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
12m
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
12m
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
12m
Talk
Probabilistic Inference Using Generators: the Statues Algorithm
LAFI
Pierre Denisindependent scholar
Link to publication DOI Pre-print Media Attached File Attached
19:30 - 20:00
19:30
30m
Social Event
Sunday Hallway Time
Workshops and Co-located Events

Call for Extended Abstracts (extended deadline)

=====================================================================

                 Call for Extended Abstracts

                          LAFI 2021
         POPL 2021 workshop on Languages for Inference 

                        January 17, 2021
          https://popl21.sigplan.org/home/lafi-2021

           Submission deadline on October 30, 2020 (extended).

====================================================================

***** Submission Summary *****

Deadline: October 30, 2020 (extended) Link: https://lafi21.hotcrp.com/ Format: extended abstract (2 pages + references)

***** Call for Extended Abstracts *****

Inference concerns re-calibrating program parameters based on observed data, and has gained wide traction in machine learning and data science. Inference can be driven by probabilistic analysis and simulation, and through back-propagation and differentiation. Languages for inference offer built-in support for expressing probabilistic models and inference methods as programs, to ease reasoning, use, and reuse. The recent rise of practical implementations as well as research activity in inference-based programming has renewed the need for semantics to help us share insights and innovations.

This workshop aims to bring programming-language and machine-learning researchers together to advance all aspects of languages for inference. Topics include but are not limited to:

  • design of programming languages for inference and/or differentiable programming;

  • inference algorithms for probabilistic programming languages, including ones that incorporate automatic differentiation;

  • automatic differentiation algorithms for differentiable programming languages;

  • probabilistic generative modeling and inference;

  • variational and differential modeling and inference;

  • semantics (axiomatic, operational, denotational, games, etc) and types for inference and/or differentiable programming;

  • efficient and correct implementation;

  • and last but not least, applications of inference and/or differentiable programming.

Two years ago, we explicitly expanded the focus of the workshop from statistical probabilistic programming to encompass differentiable programming for statistical machine learning. This change seemed well-received by the community, and we want to continue it this year in an effort to extend the strong ties between programming language-based machine learning and the POPL community.

We expect this workshop to be informal, and our goal is to foster collaboration and establish common ground. Thus, the proceedings will not be a formal or archival publication, and we expect to spend only a portion of the workshop day on traditional research talks. Nevertheless, as a concrete basis for fruitful discussions, we call for extended abstracts describing specific and ideally ongoing work on probabilistic and differential programming languages, semantics, and systems.

***** Submission guidelines *****

Submission deadline on October 30, 2020 (extended).

Submission link: https://lafi21.hotcrp.com/

Extended abstracts are up to 2 pages in PDF format, excluding references.

In line with the SIGPLAN Republication Policy, inclusion of extended abstracts in the program is not intended to preclude later formal publication.