Denny Wu
28 papers · 2019–2025 · 5 conferences · across top CS/AI conferences
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Keywords
neural network
(9)
representation learning
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gradient descent
(4)
sample complexity
(3)
single-index model
(3)
feature learning
(3)
stochastic gradient descent
(3)
ridge regression
(2)
mean-field langevin dynamics
(2)
neural network optimization
(2)
two-layer neural network
(2)
convergence analysis
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global convergence
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mean field theory
(1)
in-context learning
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gradient boosting
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convex optimization
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nonlinear regression
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binary classification
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probability distribution
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Papers
Nonlinear transformers can perform inference-time feature learning
ICML 2025
Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon
COLT 2025
Learning Compositional Functions with Transformers from Easy-to-Hard Data
COLT 2025
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics
ICLR 2025
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
ICML 2025
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
COLT 2024
Nonlinear spiked covariance matrices and signal propagation in deep neural networks
COLT 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
NIPS 2024
SILVER: Single-loop variance reduction and application to federated learning
ICML 2024
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
NIPS 2024
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data
ICLR 2024
Why is parameter averaging beneficial in SGD? An objective smoothing perspective
AISTATS 2024
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
NIPS 2023
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond
NIPS 2023
Gradient-Based Feature Learning under Structured Data
NIPS 2023
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
ICML 2023
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics
ICLR 2023
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction
NIPS 2023
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
NIPS 2022
Convex Analysis of the Mean Field Langevin Dynamics
AISTATS 2022
Understanding the Variance Collapse of SVGD in High Dimensions
ICLR 2022
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization
ICLR 2022
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
NIPS 2022
When does preconditioning help or hurt generalization?
ICLR 2021
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis
NIPS 2021
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
ICLR 2020
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression
NIPS 2020
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
ICLR 2019