7 papers
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…
Comparative Analysis of Barrier-like Function Methods for Reach-Avoid Verification in Stochastic Discrete-Time Systems
Zhipeng Cao, Peixin Wang, Luke Ong +3
In this paper, we compare several representative barrier-like conditions from the literature for infinite-horizon reach-avoid verification of stochastic discrete-time systems. Our…
PAC One-Step Safety Certification for Black-Box Discrete-Time Stochastic Systems
Taoran Wu, Dominik Wagner, Jingduo Pan +3
This paper investigates the problem of safety certification for black-box discrete-time stochastic systems, where both the system dynamics and disturbance distributions are unknown…