Work

Sampling for Bayesian Program Learning

Year: 2016

Type: conference-paper

Abstract: Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the samples approximate the true posterior, and have empirical results showing the algorithm is efficient in practice, evaluating our approach on 22 program learning problems in the domains of text editing and computer-aided programming.

Source: DSpace@MIT (Massachusetts Institute of Technology)

Authors: Kevin Ellis, Armando Solar-Lezama, Joshua B. Tenenbaum

Institution: Massachusetts Institute of Technology

Language: English

Topic: Logic, programming, and type systems

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: SHF: Medium: Collaborative Research: Marrying program analysis and numerical search, G1523888516, G230078430, G3392305090, G3616359867, G4698562369

OpenAlex ID: W2550492213

FWCI: 3.212

Cites: 24

Cited by: 23

Related to: 10