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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Related Experiment Video

Updated: May 5, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Structure-informed Risk Minimization for Robust Ensemble Learning.

Fengchun Qiao1, Yanlin Chen1, Xi Peng1

  • 1DeepREAL Lab, Department of Computer and Information Sciences, University of Delaware, DE, USA.

Proceedings of Machine Learning Research
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Structure-informed Risk Minimization (SRM) learns robust ensemble weights for improved generalization under distribution shifts. This method outperforms existing strategies in out-of-distribution settings by incorporating structural information, avoiding over-pessimism.

Related Experiment Videos

Last Updated: May 5, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Ensemble learning enhances generalization but struggles with distribution shifts.
  • Current methods optimize weights on validation data, failing in out-of-distribution (OoD) scenarios.
  • Out-of-distribution generalization remains a critical challenge in machine learning.

Purpose of the Study:

  • To develop a principled framework for learning robust ensemble weights.
  • To improve out-of-distribution generalization without access to test data.
  • To mitigate the limitations of existing ensemble combination strategies.

Main Methods:

  • Proposed Structure-informed Risk Minimization (SRM) framework inspired by Distributionally Robust Optimization (DRO).
  • Incorporated structural information of training distributions into uncertainty sets.
  • Developed a computationally efficient optimization algorithm with theoretical guarantees.

Main Results:

  • SRM learns robust ensemble weights by considering plausible real-world distribution shifts.
  • The approach avoids the over-pessimism often associated with worst-case optimization.
  • Demonstrated superior OoD generalization compared to existing methods across diverse benchmarks.

Conclusions:

  • SRM offers a principled and effective approach to enhance ensemble robustness under distribution shifts.
  • The framework provides a practical solution for improving generalization in unseen data distributions.
  • SRM represents a significant advancement in out-of-distribution generalization for ensemble learning.