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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

NodeFlow: Towards End-to-End Flexible Probabilistic Regression on Tabular Data.

Patryk Wielopolski¹, Oleksii Furman¹, Maciej Zięba^1,2

¹Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.

Entropy (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

NodeFlow enhances probabilistic regression on tabular data by combining Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). This framework achieves state-of-the-art results in complex regression tasks.

Keywords:

decision tree ensembles neural decision tree normalizing flows probabilistic regression tabular data

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Area of Science:

Machine Learning
Statistical Modeling

Background:

Traditional parametric methods struggle with arbitrary probabilistic distributions in tabular data.
Neural Oblivious Decision Ensembles (NODEs) offer powerful non-linear modeling.
Conditional Continuous Normalizing Flows (CNFs) enable flexible density estimation.

Purpose of the Study:

Introduce NodeFlow, a novel framework for probabilistic regression on tabular data.
Improve modeling of complex probabilistic distributions.
Provide a scalable and easy-to-implement solution.

Main Methods:

Combine NODEs for feature representation with CNFs for density estimation.
Utilize NODE outputs to condition the CNF.
Employ gradient-based learning for end-to-end optimization.

Main Results:

Achieved state-of-the-art performance in multivariate probabilistic regression.
Demonstrated strong performance in univariate regression tasks.
Ablation studies validated key design choices.

Conclusions:

NodeFlow offers a flexible and high-performing solution for probabilistic regression.
The framework is suitable for both practitioners and researchers.
Opens new research directions in probabilistic modeling for tabular data.