Top-Down Synthesis for Library Learning

1 code implementation • 29 Nov 2022 • Matthew Bowers, Theo X. Olausson, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, Armando Solar-Lezama

This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL).

Language Models Can Teach Themselves to Program Better

1 code implementation • 29 Jul 2022 • Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai

We show that it is possible for an LM to synthesize programming problems and solutions, which are filtered for correctness by a Python interpreter.

Code Generation

Representing Partial Programs with Blended Abstract Semantics

no code implementations • ICLR 2021 • Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next.

Program Synthesis