Assistant Professor, The Chinese University of Hong Kong, Shenzhen
3 papers at NeurIPS 2025
We propose Ensemble++, a scalable framework that achieves the low regret of Thompson Sampling using a tiny, computationally-efficient ensemble, making it practical for large-scale models.
