107 citations · 1k across the 45 of their papers we have counts for
9 papers · 1 filter
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpre…
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt +4
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to le…
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin +2
Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program sy…
Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh +2
We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent…
Program Repair via Direct State Manipulation
Qinheping Hu, Isaac Evavold, Roopsha Samanta +2
The goal of program repair is to automatically fix programs to meet a specification. We propose a new specification mechanism, direct manipulation, in which the programmer can visu…
Learning and analyzing vector encoding of symbolic representations
Roland Fernandez, Asli Celikyilmaz, Rishabh Singh +1
We present a formal language with expressions denoting general symbol structures and queries which access information in those structures. A sequence-to-sequence network processing…