On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

• URL: http://arxiv.org/abs/2203.16481v1
• Date: Wed, 30 Mar 2022 17:17:50 GMT
• Title: On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
• Authors: Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
• Abstract summary: We show that explicitly accounting for aleatoric uncertainty significantly improves the performance of Bayesian neural networks. We find that a cold posterior, tempered by a power greater than one, often more honestly reflects our beliefs about aleatoric uncertainty than no tempering.
• Score: 47.13680267076843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aleatoric uncertainty captures the inherent randomness of the data, such as measurement noise. In Bayesian regression, we often use a Gaussian observation model, where we control the level of aleatoric uncertainty with a noise variance parameter. By contrast, for Bayesian classification we use a categorical distribution with no mechanism to represent our beliefs about aleatoric uncertainty. Our work shows that explicitly accounting for aleatoric uncertainty significantly improves the performance of Bayesian neural networks. We note that many standard benchmarks, such as CIFAR, have essentially no aleatoric uncertainty. Moreover, we show data augmentation in approximate inference has the effect of softening the likelihood, leading to underconfidence and profoundly misrepresenting our honest beliefs about aleatoric uncertainty. Accordingly, we find that a cold posterior, tempered by a power greater than one, often more honestly reflects our beliefs about aleatoric uncertainty than no tempering -- providing an explicit link between data augmentation and cold posteriors. We show that we can match or exceed the performance of posterior tempering by using a Dirichlet observation model, where we explicitly control the level of aleatoric uncertainty, without any need for tempering.

Related papers

• Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning [0.19418036471925312]
  We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning. Our framework enhances the flexibility of error distribution modeling by incorporating additional higher-order moment, particularly kurtosis.
  arXiv  Detail & Related papers  (2024-08-05T08:12:25Z)
• One step closer to unbiased aleatoric uncertainty estimation [71.55174353766289]
  We propose a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
  arXiv  Detail & Related papers  (2023-12-16T14:59:11Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition [7.811916700683125]
  We introduce a bias-variance decomposition for proper scores, giving rise to the Bregman Information as the variance term. We showcase the practical relevance of this decomposition on several downstream tasks, including model ensembles and confidence regions.
  arXiv  Detail & Related papers  (2022-10-21T21:24:37Z)
• A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement [7.6146285961466]
  In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods. We show that there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty. We expect that our formulation and results help practitioners and researchers choose uncertainty methods and expand the use of disentangled uncertainties.
  arXiv  Detail & Related papers  (2022-04-20T08:41:37Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression [91.3373131262391]
  Uncertainty is the only certainty there is. Traditionally, the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. How to model the uncertainty within the present-day technologies for regression remains an open issue.
  arXiv  Detail & Related papers  (2021-03-25T06:56:09Z)
• The Hidden Uncertainty in a Neural Networks Activations [105.4223982696279]
  The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution correlates with a model's epistemic uncertainty, thus indicating its ability to generalise to novel inputs.
  arXiv  Detail & Related papers  (2020-12-05T17:30:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.