Third-year Ph.D. candidate at EECS, University of Michigan, Ann Arbor. My research interests lie primarily in machine learning algorithms and their theory. I’ve been working on Learning from Label Proportions in the first 1.5 years of my Ph.D.. Now I’m working on an embedding framework for Extreme Classification.

Publications:
Clayton Scott and Jianxin Zhang, Learning from Label Proportions: A Mutual Contamination Framework, Advances in Neural Information Processing Systems (NeurIPS), 2020.
Jianxin Zhang, Yutong Wang, Clayton Scott, Learning from Label Proportions by Learning with Label Noise, Advances in Neural Information Processing Systems (NeurIPS), 2022