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20242026
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6 papers · 1 filter

eess.SY2026

Converse Barrier Certificates for Set-Based Stochastic Reach-Avoid Verification

Bai Xue, C. -H. Luke Ong

Recent work established sufficient and necessary barrier-like conditions for infinite-horizon reach-avoid verification of stochastic discrete-time systems from a single initial sta…

eess.SY2026

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…

eess.SY2026

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 (…

eess.SY2026

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…

eess.SY2026

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,…

cs.LG2026

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…