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20122026
most citedVerifiable Reinforcement Learning via Policy Extraction

159 citations · 646 across the 48 of their papers we have counts for

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22 papers · 1 filter

cs.PL2022★ 33 cited

Top-Down Synthesis for Library Learning

Matthew Bowers, Theo X. Olausson, Lionel Wong +4

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 spe…

cs.PL2022★ 1 cited

Searching Entangled Program Spaces

James Koppel, Zheng Guo, Edsko de Vries +2

Many problem domains, including program synthesis and rewrite-based optimization, require searching astronomically large spaces of programs. Existing approaches often rely on build…

cs.PL2022

Metric Program Synthesis

John Feser, Isil Dillig, Armando Solar-Lezama

We present a new domain-agnostic synthesis technique for generating programs from input-output examples. Our method, called metric program synthesis, relaxes the well-known observa…

cs.PL2020★ 3 cited

Representing Partial Programs with Blended Abstract Semantics

Maxwell Nye, Yewen Pu, Matthew Bowers +3

Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior o…

cs.PL2020★ 3 cited

Automatically Deriving Control-Flow Graph Generators from Operational Semantics

James Koppel, Jackson Kearl, Armando Solar-Lezama

We develop the first theory of control-flow graphs from first principles, and use it to create an algorithm for automatically synthesizing many variants of control-flow graph gener…

cs.PL2019★ 2 cited

Synthesizing Queries via Interactive Sketching

Osbert Bastani, Xin Zhang, Armando Solar-Lezama

We propose a novel approach to program synthesis, focusing on synthesizing database queries. At a high level, our proposed algorithm takes as input a sketch with soft constraints e…