Talk by Jan Hoffmann: Resource Analysis for Probabilistic Programs

2018.04.12 | Malene Bisgaard Blaabjerg Andersen

Date Thu 19 Apr
Time 14:00 15:00
Location 5335-295 Nygaard


This talk presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials of the inputs.  The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs.  It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions.  An advantage of the technique is that it combines the clarity and compositionality of a weakest-precondition calculus with the efficient automation of AARA.  As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails.  Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes.  The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms.  Experimental results indicate that the derived constant factors in the bounds are very precise and even optimal for many programs.

 Short bio:

Jan Hoffmann is a Tenure-Track Assistant Professor of Computer Science at Carnegie Mellon University.  He received his PhD in 2011 from LMU Munich under the direction of Martin Hofmann.  His research interests are in the intersection of programming languages and verification with a focus on quantitative properties.  He is known for his work on automatic static resource analysis and the design and implementation of Resource Aware ML. Hoffmann's full CV and publication list can be found at .



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