Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Yingzhen, Li, Ping |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
by: Yang, Yingzhen, et al.
Published: (2024)
by: Yang, Yingzhen, et al.
Published: (2024)
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
by: Yang, Yingzhen, et al.
Published: (2024)
by: Yang, Yingzhen, et al.
Published: (2024)
Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
by: Liu, Jingyu, et al.
Published: (2025)
by: Liu, Jingyu, et al.
Published: (2025)
Finite-Particle Rates for Regularized Stein Variational Gradient Descent
by: He, Ye, et al.
Published: (2026)
by: He, Ye, et al.
Published: (2026)
Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
by: Genans, Ferdinand, et al.
Published: (2025)
by: Genans, Ferdinand, et al.
Published: (2025)
Least Squares Regression Can Exhibit Under-Parameterized Double Descent
by: Li, Xinyue, et al.
Published: (2023)
by: Li, Xinyue, et al.
Published: (2023)
Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention
by: Yang, Yingzhen
Published: (2025)
by: Yang, Yingzhen
Published: (2025)
A Computational Transition for Detecting Multivariate Shuffled Linear Regression by Low-Degree Polynomials
by: Li, Zhangsong
Published: (2025)
by: Li, Zhangsong
Published: (2025)
Minimax-Optimal Spectral Clustering with Covariance Projection for High-Dimensional Anisotropic Mixtures
by: Huang, Chengzhu, et al.
Published: (2025)
by: Huang, Chengzhu, et al.
Published: (2025)
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
by: Frei, Spencer, et al.
Published: (2022)
by: Frei, Spencer, et al.
Published: (2022)
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
by: Banerjee, Sayan, et al.
Published: (2024)
by: Banerjee, Sayan, et al.
Published: (2024)
Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon
by: Yu, Hao
Published: (2025)
by: Yu, Hao
Published: (2025)
Minimax Optimality of the Probability Flow ODE for Diffusion Models
by: Cai, Changxiao, et al.
Published: (2025)
by: Cai, Changxiao, et al.
Published: (2025)
Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
by: Zucker, Shai, et al.
Published: (2025)
by: Zucker, Shai, et al.
Published: (2025)
Online Covariance Estimation in Averaged SGD: Improved Batch-Mean Rates and Minimax Optimality via Trajectory Regression
by: Ni, Yijin, et al.
Published: (2026)
by: Ni, Yijin, et al.
Published: (2026)
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
by: Li, Gen, et al.
Published: (2023)
by: Li, Gen, et al.
Published: (2023)
Computational Complexity of Statistics: New Insights from Low-Degree Polynomials
by: Wein, Alexander S.
Published: (2025)
by: Wein, Alexander S.
Published: (2025)
Benign Overfitting in Time Series Linear Models with Over-Parameterization
by: Nakakita, Shogo, et al.
Published: (2022)
by: Nakakita, Shogo, et al.
Published: (2022)
Interactive Learning of Single-Index Models via Stochastic Gradient Descent
by: Rajaraman, Nived, et al.
Published: (2026)
by: Rajaraman, Nived, et al.
Published: (2026)
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
by: Shi, Lei, et al.
Published: (2024)
by: Shi, Lei, et al.
Published: (2024)
On Instability of Minimax Optimal Optimism-Based Bandit Algorithms
by: Praharaj, Samya, et al.
Published: (2025)
by: Praharaj, Samya, et al.
Published: (2025)
Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
by: Tamano, Shu, et al.
Published: (2026)
by: Tamano, Shu, et al.
Published: (2026)
Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces
by: Ponnoprat, Donlapark, et al.
Published: (2025)
by: Ponnoprat, Donlapark, et al.
Published: (2025)
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
by: Li, Gen, et al.
Published: (2021)
by: Li, Gen, et al.
Published: (2021)
Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
by: Yang, Jia-Qi, et al.
Published: (2025)
by: Yang, Jia-Qi, et al.
Published: (2025)
Optimal Recovery Meets Minimax Estimation
by: DeVore, Ronald, et al.
Published: (2025)
by: DeVore, Ronald, et al.
Published: (2025)
Minimax-Optimal Two-Sample Test with Sliced Wasserstein
by: Tran, Binh Thuan, et al.
Published: (2025)
by: Tran, Binh Thuan, et al.
Published: (2025)
The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli
by: Cohen, Doron, et al.
Published: (2024)
by: Cohen, Doron, et al.
Published: (2024)
Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
by: Rangriz, Parsa
Published: (2025)
by: Rangriz, Parsa
Published: (2025)
A Stein Gradient Descent Approach for Doubly Intractable Distributions
by: Lee, Heesang, et al.
Published: (2024)
by: Lee, Heesang, et al.
Published: (2024)
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
by: Patil, Pratik, et al.
Published: (2024)
by: Patil, Pratik, et al.
Published: (2024)
Minimax and Bayes Optimal Best-Arm Identification
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Improving Minimax Estimation Rates for Contaminated Mixture of Multinomial Logistic Experts via Expert Heterogeneity
by: Yan, Fanqi, et al.
Published: (2026)
by: Yan, Fanqi, et al.
Published: (2026)
Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
by: Descours, Arnaud, et al.
Published: (2025)
by: Descours, Arnaud, et al.
Published: (2025)
Minimax Optimal Estimation of Transport-Growth Pairs in Unbalanced Optimal Transport
by: Ponnoprat, Donlapark, et al.
Published: (2026)
by: Ponnoprat, Donlapark, et al.
Published: (2026)
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
by: Alfano, Carlo, et al.
Published: (2023)
by: Alfano, Carlo, et al.
Published: (2023)
Minimax Semiparametric Learning With Approximate Sparsity
by: Bradic, Jelena, et al.
Published: (2019)
by: Bradic, Jelena, et al.
Published: (2019)
Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks
by: Hashimoto, Ichiro
Published: (2025)
by: Hashimoto, Ichiro
Published: (2025)
Minimax Optimal Fair Classification with Bounded Demographic Disparity
by: Zeng, Xianli, et al.
Published: (2024)
by: Zeng, Xianli, et al.
Published: (2024)
Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
by: Zhang, Kaihong, et al.
Published: (2024)
by: Zhang, Kaihong, et al.
Published: (2024)
Similar Items
-
Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
by: Yang, Yingzhen, et al.
Published: (2024) -
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
by: Yang, Yingzhen, et al.
Published: (2024) -
Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
by: Liu, Jingyu, et al.
Published: (2025) -
Finite-Particle Rates for Regularized Stein Variational Gradient Descent
by: He, Ye, et al.
Published: (2026) -
Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
by: Genans, Ferdinand, et al.
Published: (2025)