The EAPLS Best Paper Award 2023

by Anton Wijs, May 4, 2023

The EAPLS Best Paper Award 2023 has been awarded to the paper "Automatic Alignment in Higher-Order Probabilistic Programming Languages", by Daniel Lundén, Gizem Çaylak, Fredrik Ronquist and David Broman.

The EAPLS Best Paper Award 2023 is awarded to the paper

"Automatic Alignment in Higher-Order Probabilistic Programming Languages", by Daniel Lundén, Gizem Çaylak, Fredrik Ronquist and David Broman (EECS and Digital Futures at KTH Royal Institute of Technology, Department of Bioinformatics and Genetics at Swedish Museum of Natural History, Department of Zoology at Stockholm University, Stockholm, Sweden).

This paper has been published in the proceedings of the 32nd European Symposium on Programming (ESOP 2023). Congratulations to the authors!

The abstract of the paper:

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints—relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run—they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.

Link to publication:

https://link.springer.com/chapter/10.1007/978-3-031-30044-8_20