I am a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering (ECE) at the University of Michigan, Ann Arbor. My advisors are Professors Qing Qu, and Wei Hu. My research interests are in theoretical machine learning. I also work on applications of machine learning to reconstructive spectroscopy systems and semiconductor devices, in collaboration with the research group of Professor Pei-Cheng Ku. I am grateful to be supported by the Eric and Wendy Schmidt AI Fellowship.
Before my postdoc, I completed my PhD in ECE advised by Professor Clay Scott, also at the University of Michigan. Prior to my PhD, I obtained my masters in mathematics at UC Davis.
On Classification-Calibration of Gamma-Phi Losses Yutong Wang and Clayton Scott Conference on Learning Theory, 2023. [Paper] [arXiv]
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel Yutong Wang and Clayton Scott Neural Information Processing Systems, 2022. [OpenReview][arXiv]
Learning from Label Proportions by Learning with Label Noise Jianxin Zhang, Yutong Wang, and Clayton Scott Neural Information Processing Systems, 2022. [OpenReview] [arXiv]
VC dimension of partially quantized neural networks in the overparametrized regime Yutong Wang and Clayton Scott International Conference on Learning Representations, 2022. [OpenReview] [arXiv] [Code]
An exact solver for the Weston-Watkins SVM subproblem Yutong Wang and Clayton Scott International Conference on Machine Learning 2021. [Paper] [arXiv] [Code]
Weston-Watkins Hinge Loss and Ordered Partitions Yutong Wang and Clayton Scott Neural Information Processing Systems 2020. [Paper] [arXiv]
Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics Tasha Thong, Yutong Wang, Michael D. Brooks, Christopher T. Lee, Clayton Scott, Laura Balzano, Max S. Wicha, and Justin A. Colacino Frontiers in Cell and Developmental Biology. [Paper] [Supporting technical report]