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

arXiv: https://arxiv.org/abs/2603.10582 · HTML: https://mlsanity.com/paper/2603.10582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https://mlsanity.com/paper/2506.16791/) (2025)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://mlsanity.com/paper/2311.02971/) (2023)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML](https://mlsanity.com/paper/2307.00285/) (2023)
- [Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML](https://mlsanity.com/paper/2307.08364/) (2023)
- [DivBO: Diversity-aware CASH for Ensemble Learning](https://mlsanity.com/paper/2302.03255/) (2023)
- [Regularized Neural Ensemblers](https://mlsanity.com/paper/2410.04520/) (2024)

## Built on by (most cited first, 1 of 1)

- [Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap](https://mlsanity.com/paper/2605.18696/) (2026)

## Abstract (arXiv)

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.