LAFI
Sun 19 Jan 2025 Denver, Colorado, United StatesLAFI 2025 with POPL 2025Scope of the workshop The Languages for Inference (LAFI) 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 statistical inference and/or differentiable programming Inference algorithms for probabilistic programming languages, including ones that i ... |
Wed 17 - Fri 19 January 2024 London, United KingdomLAFI 2024 with POPL 2024The Languages for Inference (LAFI) 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 statistical inference and/or differentiable programming Inference algorithms for probabilistic programming languages, including ones that incorporate automatic di ... |
Sun 15 - Sat 21 January 2023 Boston, Massachusetts, United StatesLAFI 2023 with POPL 2023The Languages for Inference (LAFI) 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 statistical inference and/or differentiable programming Inference algorithms for probabilistic programming languages, including ones that incorporate automatic di ... |
Sun 16 - Sat 22 January 2022 Philadelphia, Pennsylvania, United StatesLAFI 2022 with POPL 2022NEWS LAFI 2022 will be a purely virtual event. Detailed schedule to be posted soon. 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 statistical inference and/or differentiable programming Inference algorithms for probabilistic programming ... |
Sun 17 - Fri 22 January 2021 OnlineLAFI 2021 with POPL 2021Inference 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 ... |
Tue 21 Jan 2020 New Orleans, Louisiana, United StatesLAFI 2020 with POPL 2020Invited speaker Fritz Obermeyer, Uber AI Labs Nonstandard Interpretation in Pyro About LAFI 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 expr ... |
Sun 13 - Sat 19 January 2019 Cascais, PortugalLAFI 2019 with POPL 2019Inference 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 ... |
Mon 8 - Sat 13 January 2018 Los Angeles, California, United StatesPPS 2018 with POPL 2018Program We are excited to announce our 2018 program, including an invited talk, an invited tutorial, and the following talks and posters. For more details on the schedule, see the Program tab. The extended abstracts for the talks and posters can be found at http://pps2018.soic.indiana.edu Join our slack discussion at https://popl2018.slack.com/#pps Invited talk Erik Meijer, Facebook Software is eating the wo ... |
Tue 17 Jan 2017 PPS 2017 with POPL 2017Workshop on probabilistic programming semantics Probabilistic programming is the idea of expressing probabilistic models and inference methods as programs, to ease use and reuse. The recent rise of practical implementations as well as research activity in probabilistic programming has renewed the need for semantics to help us share insights and innovations. This workshop aims to bring programming-language and m ... |
Sat 23 Jan 2016 St. Petersburg, Florida, United StatesPPS 2016 with POPL 2016 |
Cameron Freer
Massachusetts Institute of Technology
United States
Steven Holtzen
Northeastern University
United States
Dougal Maclaurin
Chung-chieh Shan
Indiana University
United States
Jeffrey Mark Siskind
Elmore Family School of Electrical and Computer Engineering, Purdue University
United States
Christine Tasson
Sorbonne Université — LIP6
France
Jean-Baptiste Tristan