84 citations · 356 across the 59 of their papers we have counts for
6 papers · 1 filter
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
Greg Anderson, Abhinav Verma, Isil Dillig +1
We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces. A key challenge for provably safe de…
Verifying Correct Usage of Context-Free API Protocols (Extended Version)
Kostas Ferles, Jon Stephens, Isil Dillig
Several real-world libraries (e.g., reentrant locks, GUI frameworks, serialization libraries) require their clients to use the provided API in a manner that conforms to a context-f…
Optimal Neural Program Synthesis from Multimodal Specifications
Xi Ye, Qiaochu Chen, Isil Dillig +1
Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings…
Benchmarking Multimodal Regex Synthesis with Complex Structures
Xi Ye, Qiaochu Chen, Isil Dillig +1
Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes i…
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Jiayi Wei, Maruth Goyal, Greg Durrett +1
As gradual typing becomes increasingly popular in languages like Python and TypeScript, there is a growing need to infer type annotations automatically. While type annotations help…
Data Migration using Datalog Program Synthesis
Yuepeng Wang, Rushi Shah, Abby Criswell +2
This paper presents a new technique for migrating data between different schemas. Our method expresses the schema mapping as a Datalog program and automatically synthesizes a Datal…