Fri 22 Jun 2018 11:00 - 11:25 at Grand Ballroom CD - Inference for Probabilistic Programs Chair(s): Arjun Guha

We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program P into samples for a program Q, thereby avoiding the expensive sampling computation for program Q. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from P into samples of Q, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by Q, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program Q from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related P and Q are.

Fri 22 Jun

pldi-2018-papers
11:00 - 12:15: PLDI Research Papers - Inference for Probabilistic Programs at Grand Ballroom CD
Chair(s): Arjun GuhaUniversity of Massachusetts, Amherst
pldi-2018-papers11:00 - 11:25
Talk
Marco Cusumano-TownerMIT-CSAIL, Benjamin BichselETH Zurich, Switzerland, Timon Gehr, Martin VechevETH Zürich, Vikash MansingkhaMIT
Media Attached
pldi-2018-papers11:25 - 11:50
Talk
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
pldi-2018-papers11:50 - 12:15
Talk
Vikash MansingkhaMIT, Ulrich SchaechtleMassachusetts Institute of Technology, USA, Shivam Handa, Alexey Radul, Yutian ChenGoogle Deepmind, n.n., Martin RinardMassachusetts Institute of Technology
Media Attached