HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data

TMLR Paper6404 Authors

Abstract: Ensembling is commonly used in machine learning on tabular data to boost predictive performance and robustness, but larger ensembles often lead to increased hardware demand. We introduce HAPEns, a post-hoc ensembling method that explicitly balances accuracy against hardware efficiency. Inspired by multi-objective and quality diversity optimization, HAPEns constructs a diverse set of ensembles along the Pareto front of predictive performance and resource usage. Experiments on 83 tabular classification datasets show that HAPEns significantly outperforms baselines, achieving superior accuracy–efficiency trade-offs. Ablation studies further reveal that memory usage is a particularly effective objective metric. Further, we show that even a greedy ensembling algorithm can be significantly improved in this task with a static multi-objective weighting scheme.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Jian_Kang1

Submission Number: 6404