A Rate-Distortion View of Uncertainty Quantification

• URL: http://arxiv.org/abs/2406.10775v2
• Date: Tue, 18 Jun 2024 12:41:43 GMT
• Title: A Rate-Distortion View of Uncertainty Quantification
• Authors: Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski,
• Abstract summary: In supervised learning, understanding an input's proximity to the training data can help a model decide whether it has sufficient evidence for reaching a reliable prediction. We introduce Distance Aware Bottleneck (DAB), a new method for enriching deep neural networks with this property.
• Score: 36.85921945174863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In supervised learning, understanding an input's proximity to the training data can help a model decide whether it has sufficient evidence for reaching a reliable prediction. While powerful probabilistic models such as Gaussian Processes naturally have this property, deep neural networks often lack it. In this paper, we introduce Distance Aware Bottleneck (DAB), i.e., a new method for enriching deep neural networks with this property. Building on prior information bottleneck approaches, our method learns a codebook that stores a compressed representation of all inputs seen during training. The distance of a new example from this codebook can serve as an uncertainty estimate for the example. The resulting model is simple to train and provides deterministic uncertainty estimates by a single forward pass. Finally, our method achieves better out-of-distribution (OOD) detection and misclassification prediction than prior methods, including expensive ensemble methods, deep kernel Gaussian Processes, and approaches based on the standard information bottleneck.

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