Denny Wu
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Contact:dennywu [at] cs (dot) toronto (dot) edu[CV] [Google Scholar]
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I am a Ph.D. student in computer science at 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 currently working with Taiji Suzuki and Atsushi Nitanda.
Research Interests
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.
Some Recent Papers
* denotes alphabetical ordering or equal contribution. For complete list see Google Scholar.
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. [slides]
Convex analysis of the mean field langevin dynamics.
Atsushi Nitanda, Denny Wu, and Taiji Suzuki.
AISTATS 2022.
Particle dual averaging: optimization of mean field neural networks with global convergence rate analysis.
Atsushi Nitanda, Denny Wu, and Taiji Suzuki.
NeurIPS 2021.
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. [slides]
On the optimal weighted $\ell_2$ regularization in overparameterized linear regression.
Denny Wu* and Ji Xu*.
NeurIPS 2020. [slides]
