Hanlin’s very first full-sized work, Robust Coreset Construction for Distributed Machine Learning, finally got accepted into JSAC Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking. Congratulations, Hanlin! I will share the camera-ready version later, but the main idea is already captured in this arXiv version. It talks about how to use coreset (a generalization of sampled subset) to replace the full dataset in training a diverse set of models with guaranteed performance.