Abstract
Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-hoc model calibrations can improve models' uncertainty estimations without the need for retraining, and without changing the model. Our work puts forward a geometric-based approach for uncertainty estimation. Roughly speaking, we use the geometric distance of the current input from the existing training inputs as a signal for estimating uncertainty and then calibrate that signal (instead of the model's estimation) using standard post-hoc calibration techniques. We show that our method yields better uncertainty estimations than recently proposed approaches by extensively evaluating multiple datasets and models. In addition, we also demonstrate the possibility of performing our approach in near real-time applications. Our code is available at our Github [Leman and Chouraqui, 2022].
Original language | English |
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Title of host publication | Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022 |
Publisher | Association For Uncertainty in Artificial Intelligence (AUAI) |
Pages | 422-432 |
Number of pages | 11 |
ISBN (Electronic) | 9781713863298 |
State | Published - 1 Jan 2022 |
Event | 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022 - Eindhoven, Netherlands Duration: 1 Aug 2022 → 5 Aug 2022 |
Conference
Conference | 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022 |
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Country/Territory | Netherlands |
City | Eindhoven |
Period | 1/08/22 → 5/08/22 |
ASJC Scopus subject areas
- Artificial Intelligence