Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models

• URL: http://arxiv.org/abs/2602.07997v1
• Date: Sun, 08 Feb 2026 14:45:41 GMT
• Title: Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models
• Authors: TrungKhang Tran, TrungTin Nguyen, Md Abul Bashar, Nhat Ho, Richi Nayak, Christopher Drovandi,
• Abstract summary: We develop a batch minorization-maximization algorithm for softmax-gated multinomial-logistic MoE.<n>We also prove finite-sample rates for conditional density estimation and parameter recovery.<n>Experiments on biological protein--protein interaction prediction validate the full pipeline.
• Score: 40.216463162163976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture-of-Experts (MoE) architectures combine specialized predictors through a learned gate and are effective across regression and classification, but for classification with softmax multinomial-logistic gating, rigorous guarantees for stable maximum-likelihood training and principled model selection remain limited. We address both issues in the full-data (batch) regime. First, we derive a batch minorization-maximization (MM) algorithm for softmax-gated multinomial-logistic MoE using an explicit quadratic minorizer, yielding coordinate-wise closed-form updates that guarantee monotone ascent of the objective and global convergence to a stationary point (in the standard MM sense), avoiding approximate M-steps common in EM-type implementations. Second, we prove finite-sample rates for conditional density estimation and parameter recovery, and we adapt dendrograms of mixing measures to the classification setting to obtain a sweep-free selector of the number of experts that achieves near-parametric optimal rates after merging redundant fitted atoms. Experiments on biological protein--protein interaction prediction validate the full pipeline, delivering improved accuracy and better-calibrated probabilities than strong statistical and machine-learning baselines.

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