84 citations · 98 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 14 cited
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…
cs.PL2019★ 84 cited
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
Greg Anderson, Shankara Pailoor, Isil Dillig +1
In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small pert…
cs.PL2018
Learning Abstractions for Program Synthesis
Xinyu Wang, Greg Anderson, Isil Dillig +1
Many example-guided program synthesis techniques use abstractions to prune the search space. While abstraction-based synthesis has proven to be very powerful, a domain expert needs…