855 citations · 2.1k across the 21 of their papers we have counts for
5 papers · 1 filter
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Michael Tsang, Dehua Cheng, Hanpeng Liu +3
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this w…
How does this interaction affect me? Interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, Yan Liu
Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to analyze the impact of features on prediction…
Can I trust you more? Model-Agnostic Hierarchical Explanations
Michael Tsang, Youbang Sun, Dongxu Ren +1
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We…
Detecting Statistical Interactions from Neural Network Weights
Michael Tsang, Dehua Cheng, Yan Liu
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a…
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Zhengping Che, Sanjay Purushotham, Robinder Khemani +1
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of…