Probabilistic Program Abstractions

Steven Holtzen, Todd Millstein, Guy Van den Broeck
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:621-630, 2017.

Abstract

Abstraction is a fundamental tool for reasoning about complex systems. Program abstraction has been utilized to great effect for analyzing deterministic programs. At the heart of pro- gram abstraction is the relationship between a concrete program, which is difficult to ana- lyze, and an abstract program, which is more tractable. Program abstractions, however, are typically not probabilistic. We general- ize non-deterministic program abstractions to probabilistic program abstractions by explic- itly quantifying the non-deterministic choices. Our framework upgrades key definitions and properties of abstractions to the probabilistic context. We also discuss preliminary ideas for performing inference on probabilistic abstrac- tions and general probabilistic programs.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-holtzen17a, title = {Probabilistic Program Abstractions}, author = {Holtzen, Steven and Millstein, Todd and Broeck, Guy Van den}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {621--630}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/holtzen17a/holtzen17a.pdf}, url = {https://proceedings.mlr.press/r15/holtzen17a.html}, abstract = {Abstraction is a fundamental tool for reasoning about complex systems. Program abstraction has been utilized to great effect for analyzing deterministic programs. At the heart of pro- gram abstraction is the relationship between a concrete program, which is difficult to ana- lyze, and an abstract program, which is more tractable. Program abstractions, however, are typically not probabilistic. We general- ize non-deterministic program abstractions to probabilistic program abstractions by explic- itly quantifying the non-deterministic choices. Our framework upgrades key definitions and properties of abstractions to the probabilistic context. We also discuss preliminary ideas for performing inference on probabilistic abstrac- tions and general probabilistic programs.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Probabilistic Program Abstractions %A Steven Holtzen %A Todd Millstein %A Guy Van den Broeck %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-holtzen17a %I PMLR %P 621--630 %U https://proceedings.mlr.press/r15/holtzen17a.html %V R15 %X Abstraction is a fundamental tool for reasoning about complex systems. Program abstraction has been utilized to great effect for analyzing deterministic programs. At the heart of pro- gram abstraction is the relationship between a concrete program, which is difficult to ana- lyze, and an abstract program, which is more tractable. Program abstractions, however, are typically not probabilistic. We general- ize non-deterministic program abstractions to probabilistic program abstractions by explic- itly quantifying the non-deterministic choices. Our framework upgrades key definitions and properties of abstractions to the probabilistic context. We also discuss preliminary ideas for performing inference on probabilistic abstrac- tions and general probabilistic programs. %Z Reissued by PMLR on 04 October 2026.
APA
Holtzen, S., Millstein, T. & Broeck, G.V.d.. (2017). Probabilistic Program Abstractions. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:621-630 Available from https://proceedings.mlr.press/r15/holtzen17a.html. Reissued by PMLR on 04 October 2026.

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