Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc
Ensemble Selection in AutoML

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML

17 July 2023

Lennart Purucker

Lennart Schneider

Marie Anastacio

Holger Hoos

Papers citing "Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML"

5 / 5 papers shown

Title
Ensembling Finetuned Language Models for Text Classification Sebastian Pineda Arango Maciej Janowski Lennart Purucker Arber Zela Frank Hutter Josif Grabocka 23 0 0 25 Oct 2024
Dynamic Post-Hoc Neural Ensemblers Sebastian Pineda Arango Maciej Janowski Lennart Purucker Arber Zela Frank Hutter Josif Grabocka UQCV 31 0 0 06 Oct 2024
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost Jannis Maier Felix Möller Lennart Purucker 31 0 0 05 Aug 2024
Quality-Diversity Optimization: a novel branch of stochastic optimization Konstantinos Chatzilygeroudis Antoine Cully Vassilis Vassiliades Jean-Baptiste Mouret 58 80 0 08 Dec 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 81 576 0 13 Mar 2020