Fri 22 Jun 2018 11:50 - 12:15 at Grand Ballroom CD - Inference for Probabilistic Programs Chair(s): Arjun Guha

Probabilistic modeling and inference problems are often computationally intractable, so practitioners frequently design custom approximation algorithms for the problem at hand. However, current probabilistic programming languages typically provide only a small set of black-box inference algorithms that cannot be customized. This limitation renders most probabilistic programming languages unsuitable for most applications of probabilistic modeling and inference. This paper introduces novel inference meta-programming constructs for expressing custom inference algorithms in probabilistic programming languages. These constructs enable probabilistic programmers to (i) dynamically decompose inference problems into subproblems; (ii) apply inference tactics to subproblems; (iii) alternate between incorporating new data and performing inference given existing data; and (iv) explore multiple execution traces of the probabilistic program at once. Together, these inference meta-programming constructs allow probabilistic programmers to express a broad class of algorithms, including novel hybrids of gradient-based search, combinatorial search, Markov chain Monte Carlo, sequential Monte Carlo, and variational inference. Experimental results on a collection of probabilistic programs, written in the Venture probabilistic programming language, highlight the significant performance and accuracy benefits that programmable inference can deliver.

Fri 22 Jun
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11:00 - 12:15: Inference for Probabilistic ProgramsPLDI Research Papers at Grand Ballroom CD
Chair(s): Arjun GuhaUniversity of Massachusetts, Amherst
11:00 - 11:25
Talk
Incremental Inference for Probabilistic Programs
PLDI Research Papers
Marco Cusumano-TownerMIT-CSAIL, Benjamin BichselETH Zurich, Switzerland, Timon Gehr, Martin VechevETH Zürich, Vikash MansinghkaMIT
Media Attached
11:25 - 11:50
Talk
Bayonet: Probabilistic Inference for Networks
PLDI Research Papers
Timon Gehr, Sasa MisailovicUniversity of Illinois at Urbana-Champaign, USA, Petar TsankovETH Zurich, Laurent VanbeverETH Zürich, Pascal WiesmannETH Zurich, Switzerland, Martin VechevETH Zürich
Media Attached
11:50 - 12:15
Talk
Probabilistic Programming with Programmable Inference
PLDI Research Papers
Vikash MansinghkaMIT, Ulrich SchaechtleMassachusetts Institute of Technology, USA, Shivam Handa, Alexey Radul, Yutian ChenGoogle Deepmind, n.n., Martin RinardMassachusetts Institute of Technology
Media Attached