A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

arXiv:2608.24195 · stat.ML, cs.LG · Submitted 2026-08-25 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Comments: 27 pages, 6 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Interpretability — 23× in this paper · explained in 15 episodes
• Mixture of Experts — 11× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Adaptation — 7× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Mixture of Decision Trees — 5× in this paper
• Calibration — 4× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) — 3× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Towards A Rigorous Science of Interpretable Machine Learning
• Mo\"ET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
• Interpretable Mixture of Experts
• Intrinsic User-Centric Interpretability through Global Mixture of Experts
• On Bayesian Softmax-Gated Mixture-of-Experts Models

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