Denny Wu
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New York University Contact:dennywu [at] nyu (dot) edu |
About Me
I am a faculty fellow at the Center for Data Science, New York University and the Flatiron Institute.
I obtained my Ph.D. in computer science from the University of Toronto and the Vector Institute, under the supervision of Jimmy Ba and Murat A. Erdogdu. Before that, I was an undergraduate student at Carnegie Mellon University advised by Ruslan Salakhutdinov. I also frequently visit the University of Tokyo and RIKEN AIP, where I am now working with Taiji Suzuki.
My current research focuses on developing a theoretical understanding (e.g., optimization and generalization) of modern machine learning systems, especially overparameterized models such as neural networks, using tools from high-dimensional statistics.
[CV] [Google Scholar]
Some Recent Papers
Nonlinear spiked covariance matrices and signal propagation in deep neural networks.
Zhichao Wang, Denny Wu, and Zhou Fan.
Preprint.
Learning in the presence of low-dimensional structure: a spiked random matrix perspective.
Jimmy Ba*, Murat A. Erdogdu*, Taiji Suzuki*, Zhichao Wang*, and Denny Wu*.
NeurIPS 2023.
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction.
Taiji Suzuki, Denny Wu, and Atsushi Nitanda.
NeurIPS 2023.
High-dimensional asymptotics of feature learning: how one gradient step improves the representation.
Jimmy Ba*, Murat A. Erdogdu*, Taiji Suzuki*, Zhichao Wang*, Denny Wu*, and Greg Yang*.
NeurIPS 2022.
Convex analysis of the mean-field Langevin dynamics.
Atsushi Nitanda, Denny Wu, and Taiji Suzuki.
AISTATS 2022.
When does preconditioning help or hurt generalization?
Shun-ichi Amari*, Jimmy Ba*, Roger Grosse*, Xuechen Li*, Atsushi Nitanda*, Taiji Suzuki*, Denny Wu*, and Ji Xu*.
ICLR 2021.
On the optimal weighted $\ell_2$ regularization in overparameterized linear regression.
Denny Wu* and Ji Xu*.
NeurIPS 2020.
