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Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks

Published in arXiv Preprint, 2020

We make several new observations about the top eigenspace of layer-wise Hessians and explained the phenomena using Kronecker factorization. The insights also led to better explicit generalization bounds for neural nets.

Recommended citation: Wu, Y., Zhu, X., Wu, C., Wang, A. and Ge, R., 2020. Dissecting hessian: Understanding common structure of hessian in neural networks. arXiv preprint arXiv:2010.04261. https://arxiv.org/abs/2010.04261

Fairness in the Assignment Problem with Uncertain Priorities

Published in arXiv Preprint, 2023

We proposed two new notions of fairness when there exists uncertainty in the piorities. We also proposed two algorithms with certain guarantees of the proposed notion of fairness.

Recommended citation: Shen, Z., Wang, Z., Zhu, X., Fain, B., & Munagala, K. (2023). Fairness in the Assignment Problem with Uncertain Priorities. arXiv preprint arXiv:2301.13804. https://arxiv.org/abs/2301.13804

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