27 citations · 52 across the 6 of their papers we have counts for
6 papers
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
Yixuan Wang, Weichao Zhou, Jiameng Fan +6
Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performances on challenging control problems. However, the potential adoption of NN con…
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Zhilu Wang +6
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs). Existing approaches that le…
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
Jiameng Fan, Wenchao Li
Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were unseen in their training environments. To address this problem, we leverage the sequential…
Adversarial Training and Provable Robustness: A Tale of Two Objectives
Jiameng Fan, Wenchao Li
We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. We formulate the training…
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Wenchao Li +2
Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neu…
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation
Jiameng Fan, Wenchao Li
An important facet of reinforcement learning (RL) has to do with how the agent goes about exploring the environment. Traditional exploration strategies typically focus on efficienc…