693 citations · 1.4k across the 33 of their papers we have counts for
33 papers
Maintaining Stability and Plasticity for Predictive Churn Reduction
George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when mo…
Dynamic Interpretable Change Point Detection
Kopal Garg, Jennifer Yu, Tina Behrouzi +2
Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to p…
Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan Arik +2
Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a bett…
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…