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.
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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 |