Publications

1. Neural (Tangent Kernel) Collapse

Mariia Seleznova, Dana Weitzner, Raja Giryes, Gitta Kutyniok, Hung-Hsu Chou. Preprint, Neural Information Processing Systems (NeurIPS), 2023.

2. More is Less: Inducing Sparsity via Overparameterization

Hung-Hsu Chou, Johannes Maly, and Holger Rauhut.  Information and Inference. 2023.

3. Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank

Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly, and Holger Rauhut.  Applied and Computational Harmonic Analysis (ACHA). 2023.

4. Robust implicit regularization via weight normalization

Hung-Hsu Chou, Holger Rauhut, Rachel Ward.  Information and Inference, 2023.

5. How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow

Hung-Hsu Chou, Johannes Maly, Dominik Stoeger. Preprint, arXiv:2308.04921. 2023.

6. Overparameterization and generalization error: weighted trigonometric interpolation

Yuege Xie, Hung-Hsu Chou, Holger Rauhut, and Rachel Ward. Society for Industrial and Applied Mathematics (SIAM) Journal on Mathematics of Data Science. 2022.

7. Non-negative Least Squares via Overparametrization

Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun. Preprint, arXiv: 2207.08437, 2022.

8. Accuracy, Efficiency and Optimization of Signal Fragmentation

Russel E. Caflisch, Hung-Hsu Chou, and Jonathan W. Siegel. Multiscale Modeling and Simulation, 18(2), 737–757, 2020.

9. Optimization of Signal Segmentation, Signal Recovery, and Limited Current

Hung-Hsu Chou. UCLA Electronic Theses and Dissertations, 2019.

10. Optimal Correlation Estimators for Quantized Signals

Michael D. Johnson, Hung-Hsu Chou, Carl R. Gwinn. The American Astronomical Society, 752(2), 135-142, 2013.