Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification.

Area of Science:

• Machine Learning
• Statistical Modeling
• Uncertainty Quantification

Background:

• Accurate uncertainty quantification is crucial for reliable machine learning predictions.
• Existing methods often struggle to balance flexibility and structural integrity in distribution prediction.
• Gaussian assumptions limit adaptability to real-world data complexities.

Purpose of the Study:

• To introduce a novel, effective, and interpretable approach for adaptive distribution prediction in regression.
• To address the limitations of current methods in capturing complex conditional distributions.
• To achieve state-of-the-art uncertainty quantification through a data-driven ensemble strategy.

Main Methods:

• Developed an ensemble multi-quantiles (EMQ) approach using boosted additive models.
• Incorporated adaptively flexible prediction of conditional distribution quantiles.
• Focused on a data-driven strategy to depart from Gaussian assumptions and discover optimal distributions.

Main Results:

• EMQ demonstrated state-of-the-art performance across extensive regression tasks from UCI datasets.
• The method achieved superior uncertainty quantification compared to recent approaches.
• Visualizations confirmed the necessity and advantages of the ensemble model structure.

Conclusions:

• The proposed EMQ method offers a flexible and effective solution for uncertainty quantification in machine learning.
• EMQ successfully balances model flexibility and structural integrity for accurate distribution prediction.
• This data-driven approach provides a robust alternative to traditional methods, enhancing generalization and reliability.