Denny Wu

According to our database, Denny Wu authored at least 30 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Pretrained transformer efficiently learns low-dimensional target functions in-context.

Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu

CoRR, 2024

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics.

Alireza Mousavi Hosseini, Denny Wu, Murat A. Erdogdu

CoRR, 2024

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit.

Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu

CoRR, 2024

SILVER: Single-loop variance reduction and application to federated learning.

Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data.

Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Nonlinear spiked covariance matrices and signal propagation in deep neural networks.

Zhichao Wang, Denny Wu, Zhou Fan

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations.

Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Why is parameter averaging beneficial in SGD? An objective smoothing perspective.

Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction.

Taiji Suzuki, Denny Wu, Atsushi Nitanda

CoRR, 2023

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond.

Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction.

Taiji Suzuki, Denny Wu, Atsushi Nitanda

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Gradient-Based Feature Learning under Structured Data.

Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems.

Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki

Proceedings of the International Conference on Machine Learning, 2023

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics.

Taiji Suzuki, Atsushi Nitanda, Denny Wu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime.

Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization.

Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu

Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding the Variance Collapse of SVGD in High Dimensions.

Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Convex Analysis of the Mean Field Langevin Dynamics.

Atsushi Nitanda, Denny Wu, Taiji Suzuki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis.

Atsushi Nitanda, Denny Wu, Taiji Suzuki

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When does preconditioning help or hurt generalization?

Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis.

Atsushi Nitanda, Denny Wu, Taiji Suzuki

CoRR, 2020

On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression.

Denny Wu, Ji Xu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond.

Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu

CoRR, 2019

Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders.

Denny Wu, Hirofumi Kobayashi, Charles Ding, Cheng Lei, Keisuke Goda, Marzyeh Ghassemi

CoRR, 2019

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator.

Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study.

Denny Wu, Yixiu Zhao, Yao-Hung Hubert Tsai, Makoto Yamada, Ruslan Salakhutdinov

CoRR, 2018

Selecting the Best in GANs Family: a Post Selection Inference Framework.

Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu

Proceedings of the 6th International Conference on Learning Representations, 2018