Advances in Uncertainty Quantification for Machine Learning

The field of machine learning is moving towards a greater emphasis on uncertainty quantification, with a focus on developing methods that can provide reliable estimates of uncertainty in complex scenarios. This is being driven by the need for more accurate and trustworthy predictions in applications such as Earth Observation, medical diagnosis, and robot localization. Researchers are exploring various approaches, including probabilistic machine learning, Bayesian inference, and robust statistics, to address the challenges of uncertainty quantification. Notable papers in this area include:

  • One paper proposes a metrological framework for uncertainty evaluation in machine learning classification models, enabling an extension of existing standards to uncertainty for nominal properties.
  • Another paper introduces a Bayesian neural network approach for remote photoplethysmography, demonstrating good uncertainty estimation capability in real-world applications. Overall, these developments have the potential to significantly improve the accuracy and reliability of machine learning models, and are likely to have a major impact on the field in the coming years.

Sources

A metrological framework for uncertainty evaluation in machine learning classification models

Probabilistic Machine Learning for Noisy Labels in Earth Observation

RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios

Temporal Dynamic Embedding for Irregularly Sampled Time Series

Robust Statistics vs. Machine Learning vs. Bayesian Inference: Insights into Handling Faulty GNSS Measurements in Field Robotics

Robust Classification with Noisy Labels Based on Posterior Maximization

An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks

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