4 citations · 4 across the 2 of their papers we have counts for
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cs.LG2024
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
Bang An, Xun Zhou, Zirui Zhou +3
The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requ…
cs.LG2024
From Model Explanation to Data Misinterpretation: A Cautionary Analysis of Post Hoc Explainers in Business Research
Tong Wang, Ronilo Ragodos, Lu Feng +2
Post hoc explainers such as SHAP and LIME are used widely in business research to interpret complex machine learning models. Although they were designed to explain model prediction…
cs.LG2022★ 4 cited
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Ronilo J. Ragodos, Tong Wang, Qihang Lin +1
While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-…