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ORCIDDenny Wu
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2020 – today
- 2024
[c22]Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu:
Why is parameter averaging beneficial in SGD? An objective smoothing perspective. AISTATS 2024: 3565-3573- [c21]Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu:
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. COLT 2024: 4009-4081 - [c20]Zhichao Wang, Denny Wu, Zhou Fan:
Nonlinear spiked covariance matrices and signal propagation in deep neural networks. COLT 2024: 4891-4957 - [c19]Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu:
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR 2024 - [c18]Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki:
SILVER: Single-loop variance reduction and application to federated learning. ICML 2024 - [i16]Zhichao Wang, Denny Wu, Zhou Fan:
Nonlinear spiked covariance matrices and signal propagation in deep neural networks. CoRR abs/2402.10127 (2024) - [i15]Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu:
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR abs/2406.01581 (2024) - [i14]Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu:
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. CoRR abs/2406.11828 (2024) - [i13]Alireza Mousavi Hosseini, Denny Wu, Murat A. Erdogdu:
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. CoRR abs/2408.07254 (2024) - 2023
- [c17]Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR 2023 - [c16]Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki:
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML 2023: 26266-26282 - [c15]Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu:
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective. NeurIPS 2023 - [c14]Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu:
Gradient-Based Feature Learning under Structured Data. NeurIPS 2023 - [c13]Taiji Suzuki, Denny Wu, Atsushi Nitanda:
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS 2023 - [c12]Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda:
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS 2023 - [i12]Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki:
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR abs/2303.02957 (2023) - [i11]Taiji Suzuki, Denny Wu, Atsushi Nitanda:
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR abs/2306.07221 (2023) - [i10]Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu:
Gradient-Based Feature Learning under Structured Data. CoRR abs/2309.03843 (2023) - 2022
- [c11]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Convex Analysis of the Mean Field Langevin Dynamics. AISTATS 2022: 9741-9757 - [c10]Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang:
Understanding the Variance Collapse of SVGD in High Dimensions. ICLR 2022 - [c9]Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR 2022 - [c8]Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang:
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. NeurIPS 2022 - [c7]Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. NeurIPS 2022 - [i9]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Convex Analysis of the Mean Field Langevin Dynamics. CoRR abs/2201.10469 (2022) - [i8]Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang:
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. CoRR abs/2205.01445 (2022) - 2021
- [c6]Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu:
When does preconditioning help or hurt generalization? ICLR 2021 - [c5]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS 2021: 19608-19621 - 2020
- [c4]Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang:
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. ICLR 2020 - [c3]Denny Wu, Ji Xu:
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. NeurIPS 2020 - [i7]Denny Wu, Ji Xu:
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. CoRR abs/2006.05800 (2020) - [i6]Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu:
When Does Preconditioning Help or Hurt Generalization? CoRR abs/2006.10732 (2020) - [i5]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR abs/2012.15477 (2020)
2010 – 2019
- 2019
- [c2]Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. ICLR (Poster) 2019 - [i4]Denny Wu, Hirofumi Kobayashi, Charles Ding, Cheng Lei, Keisuke Goda, Marzyeh Ghassemi:
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders. CoRR abs/1901.06618 (2019) - [i3]Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu:
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. CoRR abs/1906.07868 (2019) - 2018
- [c1]Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Selecting the Best in GANs Family: a Post Selection Inference Framework. ICLR (Workshop) 2018 - [i2]Denny Wu, Yixiu Zhao, Yao-Hung Hubert Tsai, Makoto Yamada, Ruslan Salakhutdinov:
"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study. CoRR abs/1802.05408 (2018) - [i1]Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Selecting the Best in GANs Family: a Post Selection Inference Framework. CoRR abs/1802.05411 (2018)
Coauthor Index
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last updated on 2024-09-30 00:06 CEST by the dblp team
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