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Found 4 papers, 1 papers with code
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
In pursuit of an answer, we exhaustively evaluate the predictive performance of 250 selection strategies besides selecting the top-n. We extensively evaluate each selection strategy over twelve implicit feedback and eight explicit feedback data sets with eleven recommender systems algorithms.
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES).
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Consequently, we compared the performance of covariance matrix adaptation evolution strategy (CMA-ES), state-of-the-art gradient-free numerical optimization, to GES on the 71 classification datasets from the AutoML benchmark for AutoGluon.
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Moreover, we present an example of using Assembled-OpenML to compare a set of ensemble techniques.
