PLDI 2021 (series) / PLDI Research Papers /
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Fri 25 Jun 2021 09:20 - 09:25 at PLDI-A - Talks 5A: Machine Learning and Probabilistic Programming
Fri 25 Jun 2021 21:20 - 21:25 at PLDI-A - Talks 5A: Machine Learning and Probabilistic Programming
Fri 25 Jun 2021 21:20 - 21:25 at PLDI-A - Talks 5A: Machine Learning and Probabilistic Programming
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric mean over 26 benchmarks. Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language.
Fri 25 JunDisplayed time zone: Eastern Time (US & Canada) change
Fri 25 Jun
Displayed time zone: Eastern Time (US & Canada) change
09:00 - 09:40 | |||
09:00 5mTalk | DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads PLDI Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University DOI | ||
09:05 5mTalk | Provable Repair of Deep Neural Networks PLDI Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis DOI Pre-print Media Attached | ||
09:10 5mTalk | DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning PLDI Kevin Ellis Cornell University, Lionel Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology DOI | ||
09:15 5mTalk | Specification Synthesis with Constrained Horn Clauses PLDI Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore DOI | ||
09:20 5mTalk | Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming PLDI Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research DOI | ||
09:25 5mTalk | Sound Probabilistic Inference via Guide Types PLDI Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin DOI | ||
09:30 5mTalk | SPPL: Probabilistic Programming with Fast Exact Symbolic Inference PLDI Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology DOI | ||
09:35 5mTalk | Quantitative Analysis of Assertion Violations in Probabilistic Programs PLDI Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology DOI | ||
21:00 - 21:40 | |||
21:00 5mTalk | DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads PLDI Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University DOI | ||
21:05 5mTalk | Provable Repair of Deep Neural Networks PLDI Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis DOI Pre-print Media Attached | ||
21:10 5mTalk | DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning PLDI Kevin Ellis Cornell University, Lionel Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology DOI | ||
21:15 5mTalk | Specification Synthesis with Constrained Horn Clauses PLDI Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore DOI | ||
21:20 5mTalk | Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming PLDI Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research DOI | ||
21:25 5mTalk | Sound Probabilistic Inference via Guide Types PLDI Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin DOI | ||
21:30 5mTalk | SPPL: Probabilistic Programming with Fast Exact Symbolic Inference PLDI Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology DOI | ||
21:35 5mTalk | Quantitative Analysis of Assertion Violations in Probabilistic Programs PLDI Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology DOI | ||