Estimating Predictive Uncertainty Under Program Data Distribution Shift
- URL: http://arxiv.org/abs/2107.10989v1
- Date: Fri, 23 Jul 2021 01:50:22 GMT
- Title: Estimating Predictive Uncertainty Under Program Data Distribution Shift
- Authors: Yufei Li, Simin Chen, Wei Yang
- Abstract summary: Well-defined uncertainty indicates whether a model's output should (or should not) be trusted.
Existing uncertainty approaches assume that testing samples from a different data distribution would induce unreliable model predictions.
- Score: 3.603932017607092
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Deep learning (DL) techniques have achieved great success in predictive
accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to
produce highly overconfident scores for even abnormal samples. Well-defined
uncertainty indicates whether a model's output should (or should not) be
trusted and thus becomes critical in real-world scenarios which typically
involves shifted input distributions due to many factors. Existing uncertainty
approaches assume that testing samples from a different data distribution would
induce unreliable model predictions thus have higher uncertainty scores. They
quantify model uncertainty by calibrating DL model's confidence of a given
input and evaluate the effectiveness in computer vision (CV) and natural
language processing (NLP)-related tasks. However, their methodologies'
reliability may be compromised under programming tasks due to difference in
data representations and shift patterns. In this paper, we first define three
different types of distribution shift in program data and build a large-scale
shifted Java dataset. We implement two common programming language tasks on our
dataset to study the effect of each distribution shift on DL model performance.
We also propose a large-scale benchmark of existing state-of-the-art predictive
uncertainty on programming tasks and investigate their effectiveness under data
distribution shift. Experiments show that program distribution shift does
degrade the DL model performance to varying degrees and that existing
uncertainty methods all present certain limitations in quantifying uncertainty
on program dataset.
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