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05/28/2023
Training Private Models That Know What They Don't Know
Training reliable deep learning models which avoid making overconfident ...
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07/25/2022
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
The lack of well-calibrated confidence estimates makes neural networks i...
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06/29/2022
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly dete...
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05/26/2022
Selective Classification Via Neural Network Training Dynamics
Selective classification is the task of rejecting inputs a model would p...
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05/20/2020
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Time series modeling techniques based on deep learning have seen many ad...
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10/29/2018
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, well-designed soft...
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11/29/2017