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

2026-03-11

AI Summary

This work addresses the challenge of deploying ensemble methods for tabular data in resource-constrained environments, where existing approaches often incur prohibitive hardware overhead. To this end, we propose HAPEns, a hardware-aware posterior ensemble method that introduces, for the first time, a hardware-aware posterior ensembling framework. By explicitly modeling memory and computational resources as optimization objectives, HAPEns leverages multi-objective optimization, quality-diversity algorithms, and a static multi-objective weighting mechanism to generate a diverse set of ensemble configurations along the Pareto frontier between predictive performance and deployment cost. Extensive experiments across 83 classification datasets demonstrate that HAPEns significantly outperforms current baselines, achieving a superior trade-off between accuracy and resource consumption.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 a static multi-objective weighting scheme.

Problem

• ensemble learning
• hardware efficiency
• tabular data
• Pareto optimization
• memory usage

Innovation

• Hardware-Aware Ensembling
• Pareto Optimization
• Tabular Data
• Multi-Objective Optimization
• Post-Hoc Ensembling