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HAPEns introduces a novel post-hoc ensembling technique specifically designed to optimize the trade-off between predictive accuracy and hardware efficiency on tabular datasets. By leveraging multi-objective optimization, this method constructs diverse ensemble models that significantly reduce deployment costs while maintaining robust performance across 83 classification tasks. This research addresses critical resource constraints in scaling machine learning applications without compromising model reliability.
HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data
arXiv:2603.10582v2 Announce Type: replace 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. Existing hardware-aware post-hoc ensembling baselines are not available, highlighting the novelty of our approach. Experiments on 83 tabular classification datasets show that HAPEns significantly outperforms baselines, finding superior trade-offs for ensemble performance and deployment cost. Ablation studies also 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 static multi-objective weighting.
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. Existing hardware-aware post-hoc ensembling baselines are not available, highlighting the novelty of our approach. Experiments on 83 tabular classification datasets show that HAPEns significantly outperforms baselines, finding superior trade-offs for ensemble performance and deployment cost. Ablation studies also 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 static multi-objective weighting.
Machine Learning (cs.LG)
arXiv:2603.10582 [cs.LG]
(or arXiv:2603.10582v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.10582 Focus to learn more arXiv-issued DOI via DataCite
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