9 papers
Cost-Aware Adaptive Conformal Inference for Runtime Assurance in Dynamic Environments
Taoran Wu, Jingduo Pan, Luke Ong +1
This paper addresses the problem of providing runtime assurance for systems operating online under unknown and potentially time-varying data distributions. We propose Cost-Aware Ad…
Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems
Bai Xue, Luke Ong, Dominik Wagner +1
Providing finite-time probabilistic safety and reach-avoid guarantees is crucial for safety-critical stochastic systems. Existing state-of-the-art barrier methods often rely on a r…
Quantitative Verification of Finite-Time Constrained Occupation Measures for Continuous-time Stochastic Systems
Bai Xue, C. -H. Luke Ong
This paper addresses the quantitative verification of finite-time constrained occupation time for stochastic continuous-time systems governed by stochastic differential equations (…
Quantitative Verification of Constrained Occupation Time for Stochastic Discrete-time Systems
Bai Xue, Peixin Wang, C. -H. Luke Ong
This paper addresses the quantitative verification of constrained occupation time in stochastic discrete-time systems, focusing on the probability of visiting a target set at least…
PAC Finite-Time Safety Guarantees for Stochastic Systems with Unknown Disturbance Distributions
Taoran Wu, Dominik Wagner, C. -H. Luke Ong +1
We investigate the problem of establishing finite-time probabilistic safety guarantees for discrete-time stochastic dynamical systems subject to unknown disturbance distributions,…
When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
Shuqi Liu, Yuzhou Cao, Lei Feng +2
Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that…