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stat.ML2025

On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts

Fanqi Yan, Huy Nguyen, Dung Le +3

The softmax-contaminated mixture of experts (MoE) model is deployed when a large-scale pre-trained model, which plays the role of a fixed expert, is fine-tuned for learning downstr…

stat.ML2025

Quadratic Gating Mixture of Experts: Statistical Insights into Self-Attention

Pedram Akbarian, Huy Nguyen, Xing Han +1

Mixture of Experts (MoE) models are well known for effectively scaling model capacity while preserving computational overheads. In this paper, we establish a rigorous relation betw…

stat.ML2025

Statistical Advantages of Perturbing Cosine Router in Mixture of Experts

Huy Nguyen, Pedram Akbarian, Trang Pham +3

The cosine router in Mixture of Experts (MoE) has recently emerged as an attractive alternative to the conventional linear router. Indeed, the cosine router demonstrates favorable…

stat.ML2024

Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?

Huy Nguyen, Pedram Akbarian, Nhat Ho

Dense-to-sparse gating mixture of experts (MoE) has recently become an effective alternative to a well-known sparse MoE. Rather than fixing the number of activated experts as in th…

stat.ML2024

A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts

Huy Nguyen, Pedram Akbarian, TrungTin Nguyen +1

Mixture-of-experts (MoE) model incorporates the power of multiple submodels via gating functions to achieve greater performance in numerous regression and classification applicatio…