84 citations · 150 across the 11 of their papers we have counts for
11 papers
Data-Efficient and Interpretable Tabular Anomaly Detection
Chun-Hao Chang, Jinsung Yoon, Sercan Arik +2
Anomaly detection (AD) plays an important role in numerous applications. We focus on two understudied aspects of AD that are critical for integration into real-world applications.…
Extracting Expert's Goals by What-if Interpretable Modeling
Chun-Hao Chang, George Alexandru Adam, Rich Caruana +1
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify…
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Chun-Hao Chang, George Alexandru Adam, Anna Goldenberg
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlation…
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Chun-Hao Chang, Rich Caruana, Anna Goldenberg
Deployment of machine learning models in real high-risk settings (e.g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpre…
Measure-resend authenticated semi-quantum key distribution with single photons
Chun-Hao Chang, Yu-Chin Lu, Tzonelih Hwang
Yu et al. and Li et al. have proposed the measure-resend protocols of authenticated semi-quantum key distribution (ASQKD). A new measure-resend ASQKD protocol is proposed in this p…
How Interpretable and Trustworthy are GAMs?
Chun-Hao Chang, Sarah Tan, Ben Lengerich +2
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn di…