11 papers
Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity
Hongnan Ma, Yiwei Shi, Mengyue Yang +1
Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for mainta…
Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
Yiwei Shi, Zixing Song, Mengyue Yang +2
{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under st…
Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration
Yiwei Shi, Hongnan Ma, Mengyue Yang +2
In emergency response and other high-stakes societal applications, early-stage state estimates critically shape downstream outcomes. Yet, these initial state estimates-often based…
TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
Hongnan Ma, Yiwei Shi, Guanxiong Sun +2
In decision-making processes, stakeholders often rely on counterfactual explanations, which provide suggestions about what should be changed in the queried instance to alter the ou…
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering
Yiwei Shi, Jingyu Hu, Yu Zhang +4
Particle filtering is a Bayesian inference method and a fundamental tool in state estimation for dynamic systems, but its effectiveness is often limited by the constraints of the i…
Belief-Contraction-Driven Active Inverse Source Localization and Characterization
Yiwei Shi, Mengyue Yang, Qi Zhang +3
Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer l…
TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
Hongnan Ma, Kevin McAreavey, Weiru Liu
Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are cruc…
A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs
Xiaowei Liu, Kevin McAreavey, Weiru Liu
In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution…
Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation
Yiwei Shi, Muning Wen, Qi Zhang +3
Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear fe…
Explaining Reinforcement Learning: A Counterfactual Shapley Values Approach
Yiwei Shi, Qi Zhang, Kevin McAreavey +1
This paper introduces a novel approach Counterfactual Shapley Values (CSV), which enhances explainability in reinforcement learning (RL) by integrating counterfactual analysis with…
A qualitative field study on explainable AI for lay users subjected to AI cyberattacks
Kevin McAreavey, Weiru Liu, Kim Bauters +7
In this paper we present results from a qualitative field study on explainable AI (XAI) for lay users (n = 18) who were subjected to AI cyberattacks. The study was based on a custo…