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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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Showing 2019Show all

6 papers · 1 filter

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…

cs.PL2019★ 20 cited

Write, Execute, Assess: Program Synthesis with a REPL

Kevin Ellis, Maxwell Nye, Yewen Pu +3

We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search p…

cs.PL2019★ 3 cited

The Random Conditional Distribution for Higher-Order Probabilistic Inference

Zenna Tavares, Xin Zhang, Edgar Minaysan +3

The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At…

cs.PL2019

Deductive Optimization of Relational Data Storage

John K. Feser, Samuel Madden, Nan Tang +1

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical…

cs.AI2019★ 32 cited

Learning to Infer Program Sketches

Maxwell Nye, Luke Hewitt, Joshua Tenenbaum +1

Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction. The…

cs.AI2019★ 3 cited

Soft Constraints for Inference with Declarative Knowledge

Zenna Tavares, Javier Burroni, Edgar Minaysan +2

We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate. A predicate is a Boolean valued function which expresses a yes/no question a…