Ensemble Teacher

Ensemble teacher methods leverage multiple "teacher" models to train a single "student" model, aiming to improve performance, privacy, or fairness in machine learning. Current research focuses on adapting this framework to various applications, including quantum machine learning and speech recognition, exploring different ensemble strategies like knowledge distillation and elitist sampling, and addressing challenges such as fairness and differential privacy. This approach holds significant promise for enhancing model robustness, protecting sensitive data, and mitigating biases in diverse machine learning tasks.