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Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
Mark Penrod · Harrison Termotto · Varshini Reddy · Jiayu Yao · Finale Doshi-Velez · Weiwei Pan

For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from literature which uncertainty-aware models are best suited for a given dataset. Thus, we compare six uncertainty-aware deep learning models on a set of edge-case tasks: robustness to adversarial attacks as well as out-of-distribution and adversarial detection. We find that the geometry of the data sub-manifold is an important factor in determining the success of various models. Our finding suggests an interesting direction in the study of uncertainty-aware deep learning models.

Author Information

Mark Penrod (Harvard University)
Harrison Termotto (Harvard University)
Varshini Reddy (Harvard University)
Jiayu Yao (Harvard University)
Finale Doshi-Velez (Harvard University)
Weiwei Pan (Harvard University)

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