Adaptive Multi-Teacher Distillation for Enhanced Supervised Learning

Author(s): Shenggang Li

Originally published on Towards AI.

A Novel Approach for Dynamically Combining Multiple Predictive Models into a Lightweight High-Performance Student ModelPhoto by Damon Hall on Unsplash

In practical supervised learning, using a single predictive model like XGBoost, LightGBM, or Random Forest is standard. But often, combining these models boosts performance significantly. Traditional methods blend predictions from multiple models with fixed weights or logistic regression, treating each model equally across all predictions. This is easy but misses the chance to leverage each model’s specific strengths based on different situations or inputs.

To solve this limitation, I propose a novel approach: instead of static blending, I employ a lightweight neural network “student” that dynamically learns from multiple sophisticated “teacher” models simultaneously. Each teacher — like XGBoost or Random Forest — predicts probabilities, which the student uses during training. Critically, the student network also learns attention weights, which dynamically determine how much each teacher influences each specific prediction. For example, when predicting if a customer will respond to a promotion, the student might rely more on XGBoost for younger customers but prefer Random Forest predictions for older customers.

Unlike simple logistic blending, the adaptive distillation method offers two distinct advantages: Firstly, by allowing dynamic weighting for each prediction, the student model can tailor itself flexibly, capturing complex patterns no single model or static ensemble can. Secondly,… Read the full blog for free on Medium.

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Published via Towards AI