1.4k citations · 3.1k across the 30 of their papers we have counts for
14 papers · 1 filter
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana +1
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers unde…
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori +6
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However,…
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori +6
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in de…
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu +2
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while prote…
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…
Dropout as a Regularizer of Interaction Effects
Benjamin Lengerich, Eric P. Xing, Rich Caruana
We examine Dropout through the perspective of interactions. This view provides a symmetry to explain Dropout: given variables, there are possible sets of va…