Ensemble Kernel

Ensemble kernels combine multiple kernel functions within a machine learning model, aiming to improve predictive accuracy and robustness by leveraging the strengths of diverse kernel types. Current research focuses on applying ensemble kernels within Gaussian Processes and gradient-boosted regression trees, often employing Bayesian optimization to determine optimal kernel weights, and exploring their use in diverse applications like sales forecasting and graph classification. This approach demonstrates significant improvements in predictive performance across various metrics compared to single-kernel models, highlighting the value of ensemble kernels for enhancing the capabilities of existing machine learning algorithms in diverse fields.

Papers

October 10, 2024

Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model
Yonghang Zhou, Hongyi Zhu, Yidong Chai, Yuanchun Jiang, Yezheng Liu
Model Uncertainty Uncertainty Aware Deep Learning Web Attack Ensemble Kernel

April 15, 2024

March 4, 2023

AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
Lixin Cui, Ming Li, Yue Wang, Lu Bai, Edwin R. Hancock
Graph Kernel Shannon Entropy Quantum Walk R Convolution Graph Kernel Ensemble Kernel

September 30, 2022

Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan, Adam Ibrahim, Amirozhan Dehghani, Yifei Ren
Adversarial Attack Adversarial Example Model Ensemble Ensemble Kernel

July 2, 2022

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Behrang Shafei, Ruth Misener
Neural Architecture Search Bayesian Optimization Participation Constraint Black Box Optimization Feature Space Tree Ensemble Ensemble Kernel

May 23, 2022

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
Jonathan Brophy, Daniel Lowd
Gradient Boosted Tree Instance Wise Uncertainty Ensemble Kernel

Ensemble Kernel

Papers

Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach

Integrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models

AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks

Learning Robust Kernel Ensembles with Kernel Average Pooling

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees