Jingfeng Wu
29 papers · 2019–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π Conference Polyglot (9)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π€
Dynamic Duo
(17)
ποΈ
Keyword Collector
(111)
β
The Questioner
(3)
β‘
Prolific Year
(6)
π
Century Club
(29)
π₯
Unstoppable
(7)
Conferences
NIPS (9)
ICML (8)
ICLR (4)
COLT (2)
NSDI (2)
ACML (1)
AISTATS (1)
CVPR (1)
JMLR (1)
Top co-authors
Research topics
Keywords
stochastic gradient descent
(8)
linear regression
(5)
gradient descent
(4)
excess risk
(3)
iterate averaging
(3)
logistic regression
(2)
learning theory
(2)
risk bound
(2)
neural network optimization
(2)
overparameterized learning
(2)
implicit bia
(2)
catastrophic forgetting
(1)
domain adaptation
(1)
manifold regularization
(1)
transfer learning
(1)
neural tangent kernel
(1)
stochastic optimization
(1)
model selection
(1)
sample complexity
(1)
iterative optimization
(1)
Papers
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes
ICML 2025
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
ICML 2025
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
ICML 2025
How Does Critical Batch Size Scale in Pre-training?
ICLR 2025
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
NIPS 2024
Scaling Laws in Linear Regression: Compute, Parameters, and Data
NIPS 2024
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
COLT 2024
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression
ICLR 2024
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
NIPS 2024
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
ICLR 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
NIPS 2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
NIPS 2023
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
NIPS 2022
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
AISTATS 2022
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
NIPS 2022
Ship Compute or Ship Data? Why Not Both?
NSDI 2021
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
NIPS 2021
Lifelong Learning with Sketched Structural Regularization
ACML 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
Twenty Years After: Hierarchical Core-Stateless Fair Queueing
NSDI 2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems
NIPS 2021
On the Noisy Gradient Descent that Generalizes as SGD
ICML 2020
Obtaining Adjustable Regularization for Free via Iterate Averaging
ICML 2020
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
ICML 2019
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning
CVPR 2019