84 citations · 99 across the 6 of their papers we have counts for
6 papers
Determinants of hyperparameter robustness in connectome reservoir computing
Miles Walter Churchland, Raul de Palma Aristides, Jordi Garcia-Ojalvo +3
Reservoir computing provides a controlled setting for studying how recurrent network architectureshapes computation: input signals are projected into a high-dimensional state space…
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation
Chenxi Yang, Greg Anderson, Swarat Chaudhuri
We present a reinforcement learning (RL) framework in which the learned policy comes with a machine-checkable certificate of provable adversarial robustness. Our approach, called C…
Guiding Safe Exploration with Weakest Preconditions
Greg Anderson, Swarat Chaudhuri, Isil Dillig
In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present…
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…
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…
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…