Denny Wu - RankMe

57 papers A* 25A 2Journal 28Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning. CoRR Filip Kovacevic, Hong Chang Ji, Denny Wu, Mahdi Soltanolkotabi, Marco Mondelli
2026	J	`jnl`	Learning to Recall with Transformers Beyond Orthogonal Embeddings. CoRR Nuri Mert Vural, Alberto Bietti, Mahdi Soltanolkotabi, Denny Wu
2026	J	`jnl`	Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory. CoRR Juno Kim, Eshaan Nichani, Denny Wu, Alberto Bietti, Jason D. Lee
2025	J	`jnl`	Emergence and scaling laws in SGD learning of shallow neural networks. CoRR Yunwei Ren, Eshaan Nichani, Denny Wu, Jason D. Lee
2025	J	`jnl`	From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers. CoRR Ryotaro Kawata, Yujin Song, Alberto Bietti, Naoki Nishikawa, Taiji Suzuki, Samuel Vaiter, Denny Wu
2025	J	`jnl`	How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? CoRR Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki
2025	A*	`conf`	Learning Compositional Functions with Transformers from Easy-to-Hard Data. COLT Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu
2025	J	`jnl`	Learning Compositional Functions with Transformers from Easy-to-Hard Data. CoRR Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu
2025	A*	`conf`	Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. ICLR Alireza Mousavi Hosseini, Denny Wu, Murat A. Erdogdu
2025	A*	`conf`	Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. COLT Margalit Glasgow, Denny Wu, Joan Bruna
2025	A*	`conf`	Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation. ICML Juno Kim, Denny Wu, Jason D. Lee, Taiji Suzuki
2025	J	`jnl`	Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation. CoRR Juno Kim, Denny Wu, Jason Lee, Taiji Suzuki
2025	A*	`conf`	Nonlinear transformers can perform inference-time feature learning. ICML Naoki Nishikawa, Yujin Song, Kazusato Oko, Denny Wu, Taiji Suzuki
2025	J	`jnl`	Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time. CoRR Margalit Glasgow, Denny Wu, Joan Bruna
2025	J	`jnl`	Understanding the Mechanisms of Fast Hyperparameter Transfer. CoRR Nikhil Ghosh, Denny Wu, Alberto Bietti
2025	J	`jnl`	When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective. CoRR Alireza Mousavi Hosseini, Clayton Sanford, Denny Wu, Murat A. Erdogdu
2024	A*	`conf`	Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu
2024	J	`jnl`	Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. CoRR Alireza Mousavi Hosseini, Denny Wu, Murat A. Erdogdu
2024	A*	`conf`	Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. COLT Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
2024	J	`jnl`	Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. CoRR Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
2024	A*	`conf`	Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. NeurIPS Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu
2024	J	`jnl`	Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu
2024	A*	`conf`	Nonlinear spiked covariance matrices and signal propagation in deep neural networks. COLT Zhichao Wang, Denny Wu, Zhou Fan
2024	J	`jnl`	Nonlinear spiked covariance matrices and signal propagation in deep neural networks. CoRR Zhichao Wang, Denny Wu, Zhou Fan
2024	A*	`conf`	Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context. NeurIPS Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
2024	J	`jnl`	Pretrained transformer efficiently learns low-dimensional target functions in-context. CoRR Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
2024	A*	`conf`	SILVER: Single-loop variance reduction and application to federated learning. ICML Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki
2024	A	`conf`	Why is parameter averaging beneficial in SGD? An objective smoothing perspective. AISTATS Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu
2023	J	`jnl`	Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR Taiji Suzuki, Denny Wu, Atsushi Nitanda
2023	A*	`conf`	Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda
2023	A*	`conf`	Gradient-Based Feature Learning under Structured Data. NeurIPS Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
2023	J	`jnl`	Gradient-Based Feature Learning under Structured Data. CoRR Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
2023	A*	`conf`	Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective. NeurIPS Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu
2023	A*	`conf`	Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS Taiji Suzuki, Denny Wu, Atsushi Nitanda
2023	A*	`conf`	Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
2023	J	`jnl`	Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
2023	A*	`conf`	Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR Taiji Suzuki, Atsushi Nitanda, Denny Wu
2022	A	`conf`	Convex Analysis of the Mean Field Langevin Dynamics. AISTATS Atsushi Nitanda, Denny Wu, Taiji Suzuki
2022	J	`jnl`	Convex Analysis of the Mean Field Langevin Dynamics. CoRR Atsushi Nitanda, Denny Wu, Taiji Suzuki
2022	A*	`conf`	High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. NeurIPS Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
2022	J	`jnl`	High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. CoRR Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
2022	A*	`conf`	Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu
2022	A*	`conf`	Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. NeurIPS Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu
2022	A*	`conf`	Understanding the Variance Collapse of SVGD in High Dimensions. ICLR Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
2021	A*	`conf`	Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS Atsushi Nitanda, Denny Wu, Taiji Suzuki
2021	A*	`conf`	When does preconditioning help or hurt generalization? ICLR Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2020	A*	`conf`	Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. ICLR Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang
2020	A*	`conf`	On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. NeurIPS Denny Wu, Ji Xu
2020	J	`jnl`	On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. CoRR Denny Wu, Ji Xu
2020	J	`jnl`	Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR Atsushi Nitanda, Denny Wu, Taiji Suzuki
2020	J	`jnl`	When Does Preconditioning Help or Hurt Generalization? CoRR Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2019	J	`jnl`	Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders. CoRR Denny Wu, Hirofumi Kobayashi, Charles Ding, Cheng Lei, Keisuke Goda, Marzyeh Ghassemi
2019	—	`conf`	Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. ICLR (Poster) Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
2019	J	`jnl`	Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. CoRR Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu
2018	J	`jnl`	"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study. CoRR Denny Wu, Yixiu Zhao, Yao-Hung Hubert Tsai, Makoto Yamada, Ruslan Salakhutdinov
2018	—	`conf`	Selecting the Best in GANs Family: a Post Selection Inference Framework. ICLR (Workshop) Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
2018	J	`jnl`	Selecting the Best in GANs Family: a Post Selection Inference Framework. CoRR Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu