5 papers
On the Limits of Sampling-Based Reachability: Geometry, Dynamics, and Sample Complexity
Jixian Liu, Ihab Tabbara, Hussein Sibai +1
Reachability analysis is central to safety-critical control, robotics, and neural network verification, but classical computational methods, such as Hamilton--Jacobi reachability a…
Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
Zhuo Ouyang, Jixian Liu, Enrique Mallada
Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity…
Safety-Critical Control via Recurrent Tracking Functions
Jixian Liu, Enrique Mallada
This paper addresses the challenge of synthesizing safety-critical controllers for high-order nonlinear systems, where constructing valid Control Barrier Functions (CBFs) remains c…
Recurrent Control Barrier Functions: A Path Towards Nonparametric Safety Verification
Jixian Liu, Enrique Mallada
Ensuring the safety of complex dynamical systems often relies on Hamilton-Jacobi (HJ) Reachability Analysis or Control Barrier Functions (CBFs). Both methods require computing a fu…
Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression
Jixian Liu, Tao Xu, Jianping He +1
The predict-then-optimize (PTO) framework is indispensable for addressing practical stochastic decision-making tasks. It consists of two crucial steps: initially predicting unknown…