Jingfeng Wu
31 papers · 2019–2026 · 9 venues · across top CS/AI venues
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Machine Learning
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Optimization & Theory Β· Machine Learning
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Mathematics & Optimization
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Optimization
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stochastic gradient descent
(12)
gradient descent
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linear regression
(8)
implicit bia
(5)
excess risk
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logistic regression
(4)
generalization bound
(3)
neural network optimization
(3)
iterate averaging
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risk bound
(2)
neural tangent kernel
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convergence rate
(2)
benign overfitting
(2)
excess risk bound
(2)
learning theory
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non-homogeneous networks
(2)
perceptron algorithm
(2)
ridge regression
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in-context learning
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early stopping
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Papers
Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
JMLR 2026
Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract)
COLT 2026
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
ICML 2025
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes
ICML 2025
How Does Critical Batch Size Scale in Pre-training?
ICLR 2025
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
ICML 2025
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
ICLR 2024
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
NeurIPS 2024
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
COLT 2024
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
NeurIPS 2024
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression
ICLR 2024
Scaling Laws in Linear Regression: Compute, Parameters, and Data
NeurIPS 2024
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
JMLR 2023
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
ICML 2023
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
NeurIPS 2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
NeurIPS 2023
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
AISTATS 2022
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
ICML 2022
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
NeurIPS 2022
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
NeurIPS 2022
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
NeurIPS 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
COLT 2021
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
ICLR 2021
Lifelong Learning with Sketched Structural Regularization
ACML 2021
Ship Compute or Ship Data? Why Not Both?
NSDI 2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems
NeurIPS 2021
Twenty Years After: Hierarchical Core-Stateless Fair Queueing
NSDI 2021
Obtaining Adjustable Regularization for Free via Iterate Averaging
ICML 2020
On the Noisy Gradient Descent that Generalizes as SGD
ICML 2020
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning
CVPR 2019
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
ICML 2019