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A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning — arXiv:2608.24195

Source

arXiv (en)

Original

https://arxiv.org/abs/2608.24195

License (as archived)

CC0-1.0

Archived

2026-10-18T06:36:51.293Z

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20b9a66543eae54c8558713760fa8ce487a6b4041fe2aaef785a8a8f93f2da84

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A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning — arXiv:2608.24195 — arXiv (en), https://arxiv.org/abs/2608.24195 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2e382de4ebae (Metraipolis archive dc_2e382de4ebae, sha256 20b9a66543eae54c8558713760fa8ce487a6b4041fe2aaef785a8a8f93f2da84)

@misc{dc2e382de4ebae,
  title = {A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning — arXiv:2608.24195},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.24195},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2e382de4ebae; sha256 20b9a66543eae54c8558713760fa8ce487a6b4041fe2aaef785a8a8f93f2da84},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.24195. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through an input-dependent gating mechanism. Existing interpretable MoE approaches, such as Mixture of Decision Trees (MoDT), achieve transparency by employing homogeneous decision-tree experts, but this restricts the model to a single inductive bias across all regions of the feature space. We extend the MoDT framework by introducing heterogeneous expert families comprising decision trees, linear support vector machines, and quadratic discriminant analysis under a common probabilistic gating mechanism. To ensure coherent likelihood-based inference, non-probabilistic experts are calibrated to produce conditional class probabilities, allowing parameter estimation within the generalized Expectation-Maximization framework of MoDT. We further establish theoretical monotone ascent guarantees for the proposed heterogeneous gating updates, providing a justification for the optimization procedure. Experiments on a diverse collection of synthetic and real-world benchmark datasets demonstrate that the proposed framework adaptively specializes experts according to local data geometry, yielding interpretable expert assignments while achieving predictive performance competitive with homogeneous MoDT and Random Forests. The proposed approach combines interpretability, adaptive inductive bias selection, and probabilistic coherence within a unified mixture-of-experts framework.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.24195 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T06:28:52.344Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).