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 (ICLR (Workshop), 2018)

"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study. - Denny Wu; Yixiu Zhao; Yao-Hung Hubert Tsai; Makoto Yamada; Ruslan Salakhutdinov (CoRR, 2018)

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 (ICLR (Poster), 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)

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. - Xuechen Li; Denny Wu; Lester Mackey; Murat A. Erdogdu (CoRR, 2019)

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Denny Wu; Tianzong Zhang (ICLR, 2020)

On the Optimal Weighted $ ell_2$ Regularization in Overparameterized Linear Regression. - Denny Wu; Ji Xu (NeurIPS, 2020)

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (CoRR, 2020)

When does preconditioning help or hurt generalization? - Shun-ichi Amari; Jimmy Ba; Roger Baker Grosse; Xuechen Li 005; Atsushi Nitanda; Taiji Suzuki; Denny Wu; Ji Xu (ICLR, 2021)

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (NeurIPS, 2021)

Convex Analysis of the Mean Field Langevin Dynamics. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (AISTATS, 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 (ICLR, 2022)

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. - Kazusato Oko; Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 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 (NeurIPS, 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 (NeurIPS, 2022)

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. - Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2023)

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. - Atsushi Nitanda; Kazusato Oko; Denny Wu; Nobuhito Takenouchi; Taiji Suzuki (ICML, 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 (NeurIPS, 2023)

Gradient-Based Feature Learning under Structured Data. - Alireza Mousavi Hosseini; Denny Wu; Taiji Suzuki; Murat A. Erdogdu (NeurIPS, 2023)

Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (NeurIPS, 2023)

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. - Taiji Suzuki; Denny Wu; Kazusato Oko; Atsushi Nitanda (NeurIPS, 2023)

Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (CoRR, 2023)

Why is parameter averaging beneficial in SGD? An objective smoothing perspective. - Atsushi Nitanda; Ryuhei Kikuchi; Shugo Maeda; Denny Wu (AISTATS, 2024)

Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (COLT, 2024)

Nonlinear spiked covariance matrices and signal propagation in deep neural networks. - Zhichao Wang; Denny Wu; Zhou Fan (COLT, 2024)

Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. - Atsushi Nitanda; Kazusato Oko; Taiji Suzuki; Denny Wu (ICLR, 2024)

SILVER: Single-loop variance reduction and application to federated learning. - Kazusato Oko; Shunta Akiyama; Denny Wu; Tomoya Murata; Taiji Suzuki (ICML, 2024)

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. - Jason D. Lee; Kazusato Oko; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. - Margalit Glasgow; Denny Wu; Joan Bruna (COLT, 2025)

Learning Compositional Functions with Transformers from Easy-to-Hard Data. - Zixuan Wang; Eshaan Nichani; Alberto Bietti; Alex Damian; Daniel Hsu 001; Jason D. Lee; Denny Wu (COLT, 2025)

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. - Alireza Mousavi Hosseini; Denny Wu; Murat A. Erdogdu (ICLR, 2025)

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation. - Juno Kim; Denny Wu; Jason Lee; Taiji Suzuki (CoRR, 2025)

When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective. - Alireza Mousavi Hosseini; Clayton Sanford; Denny Wu; Murat A. Erdogdu (CoRR, 2025)

Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time. - Margalit Glasgow; Denny Wu; Joan Bruna (CoRR, 2025)

Emergence and scaling laws in SGD learning of shallow neural networks. - Yunwei Ren; Eshaan Nichani; Denny Wu; Jason D. Lee (CoRR, 2025)