HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data
This addresses hardware deployment costs for machine learning practitioners using tabular data, though it is incremental as it builds on existing ensembling and optimization methods.
The paper tackles the problem of balancing predictive performance and hardware efficiency in ensembling for tabular data, introducing HAPEns, which constructs diverse ensembles along the Pareto front and significantly outperforms baselines on 83 datasets.
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 a static multi-objective weighting scheme.