Jiyi Chen
Amirreza “Amir” Eshraghi
Leelavathi “Leela” Raja
I am an assistant professor in the Computer Science Department at Illinois Tech. I am also a member of the IDEAL Institute.
Previously, I was a Postdoctoral Research Fellow supported by the Eric and Wendy Schmidt AI Fellowship in the Department of Electrical and Computer Engineering (ECE) at the University of Michigan, Ann Arbor. Before my postdoc, I completed my PhD in ECE at the University of Michigan. Prior to that, I spent two years in the Mathematics Department of UC Davis where I obtained my masters.
Uniquely multiclass phenomena • Theory of overparametrized learning • AI & ML for science • Uncertainty estimation • Privacy-preserving machine learning.
I’m fortunate to work with the following group of talented students. See the group photos page.
Jiyi Chen
Amirreza “Amir” Eshraghi
Leelavathi “Leela” Raja
"-> next position" (if known).
* denotes equal contribution.
Brenier Isotonic Regression Han Bao, Amirreza Eshraghi, and Yutong Wang Accepted to Artificial Intelligence and Statistics, 2026. [arXiv coming soon]
The Implicit Bias of Gradient Descent on Separable Multiclass Data Hrithik Ravi, Clayton Scott, Daniel Soudry, and Yutong Wang Neural Information Processing Systems, 2024. [OpenReview] [arXiv]
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation Jiyi Chen*, Pengyu Li*, Yutong Wang, Pei-Cheng Ku, and Qing Qu APL Machine Learning, vol. 2, no. 3, pp. 036106, 2024. [Paper] [arXiv]
Unified Binary and Multiclass Margin-Based Classification Yutong Wang and Clayton Scott Journal of Machine Learning Research, vol. 25, no. 143, pp. 1–51, 2024. [Paper] [arXiv]
Neural Collapse in Multi-label Learning with Pick-all-label Loss Pengyu Li*, Xiao Li*, Yutong Wang, and Qing Qu International Conference on Machine Learning, 2024. [Paper] [arXiv]
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization Yutong Wang, Rishi Sonthalia, Wei Hu Artificial Intelligence and Statistics, 2024. [Paper] [arXiv]
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi, and Wei Hu International Conference on Learning Representations, 2024. [OpenReview] [arXiv]
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]
All about SVMs: