activity
20232026
most citedRepairing Deep Neural Networks Based on Behavior Imitation

1 citations · 1 across the 14 of their papers we have counts for

collaborators

14 papers

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…

cs.LG2026

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning

Jingduo Pan, Taoran Wu, Yiling Xue +1

We study stochastic minimum-cost reach-avoid reinforcement learning, where an agent must satisfy a reach-avoid specification with probability at least while minimizing expected…

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,…

eess.SY2025

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…

eess.SY2025

Efficient Verification and Falsification of ReLU Neural Barrier Certificates

Dejin Ren, Yiling Xue, Taoran Wu +1

Barrier certificates play an important role in verifying the safety of continuous-time systems, including autonomous driving, robotic manipulators and other critical applications.…

eess.SY2025

Controlled Reach-avoid Set Computation for Discrete-time Polynomial Systems via Convex Optimization

Taoran Wu, Yiling Xue, Dejin Ren +3

This paper addresses the computation of controlled reach-avoid sets (CRASs) for discrete-time polynomial systems subject to control inputs. A CRAS is a set encompassing initial sta…

eess.SY2025

Convex Computations for Controlled Safety Invariant Sets of Black-box Discrete-time Dynamical Systems

Taoran Wu, Yiling Xue, Jingduo Pan +3

Identifying controlled safety invariant sets (CSISs) is essential for safety-critical systems. This paper addresses the problem of computing CSISs for black-box discrete-time syste…

eess.SY2024

Inner-approximate Reachability Computation via Zonotopic Boundary Analysis

Dejin Ren, Zhen Liang, Chenyu Wu +3

Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems ana…

eess.SY2024

A Framework for Safe Probabilistic Invariance Verification of Stochastic Dynamical Systems

Taoran Wu, Yiqing Yu, Bican Xia +2

Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications. This paper introduces a comprehensive framework for the safe…

eess.SY2024

Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems

Yonghan Li, Chenyu Wu, Taoran Wu +2

In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in t…

cs.AI2024

UR4NNV: Neural Network Verification, Under-approximation Reachability Works!

Zhen Liang, Taoran Wu, Ran Zhao +5

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectivenes…

eess.SY2023

Reach-avoid Analysis for Sampled-data Systems with Measurement Uncertainties

Taoran Wu, Dejin Ren, Shuyuan Zhang +2

Digital control has become increasingly prevalent in modern systems, making continuous-time plants controlled by discrete-time (digital) controllers ubiquitous and crucial across i…

cs.LG2023★ 1 cited

Repairing Deep Neural Networks Based on Behavior Imitation

Zhen Liang, Taoran Wu, Changyuan Zhao +4

The increasing use of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential for exhibiting ill-behaviors. While DNN verification and test…

eess.SY2023

Provable Reach-avoid Controllers Synthesis Based on Inner-approximating Controlled Reach-avoid Sets

Jianqiang Ding, Taoran Wu, Yuping Qian +2

In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a de…