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…
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
Loc Trinh, Michael Tsang, Sirisha Rambhatla +1
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-t…
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla +1
Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the…
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Karishma Sharma, Pinar Donmez, Enming Luo +2
Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, w…