Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
This work addresses the issue of unreliable confidence estimation in neural networks for decision-making systems, presenting an incremental improvement over existing methods like isotonic regression.
The paper tackled the problem of poor calibration in neural networks, which leads to unreliable confidence scores, by introducing a bilevel optimization approach for self-calibrating training. The results showed that this method reduces calibration error while maintaining accuracy, as tested on toy and simulated datasets like Blobs, Spirals, and Blood Alcohol Concentration.
Handling uncertainty is critical for ensuring reliable decision-making in intelligent systems. Modern neural networks are known to be poorly calibrated, resulting in predicted confidence scores that are difficult to use. This article explores improving confidence estimation and calibration through the application of bilevel optimization, a framework designed to solve hierarchical problems with interdependent optimization levels. A self-calibrating bilevel neural-network training approach is introduced to improve a model's predicted confidence scores. The effectiveness of the proposed framework is analyzed using toy datasets, such as Blobs and Spirals, as well as more practical simulated datasets, such as Blood Alcohol Concentration (BAC). It is compared with a well-known and widely used calibration strategy, isotonic regression. The reported experimental results reveal that the proposed bilevel optimization approach reduces the calibration error while preserving accuracy.