Jingfeng Wu | DeepAI

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∙ 06/15/2023

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance

In federated frequency estimation (FFE), multiple clients work together ...

0 Jingfeng Wu, et al. ∙

∙ 05/19/2023

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability

Recent research has observed that in machine learning optimization, grad...

0 Jingfeng Wu, et al. ∙

∙ 03/17/2023

Fixed Design Analysis of Regularization-Based Continual Learning

We consider a continual learning (CL) problem with two linear regression...

0 Haoran Li, et al. ∙

∙ 03/03/2023

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples

This paper considers the problem of learning a single ReLU neuron with s...

1 Jingfeng Wu, et al. ∙

∙ 08/03/2022

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

We study linear regression under covariate shift, where the marginal dis...

4 Jingfeng Wu, et al. ∙

∙ 03/07/2022

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

Stochastic gradient descent (SGD) has achieved great success due to its ...

4 Difan Zou, et al. ∙

∙ 10/12/2021

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Stochastic gradient descent (SGD) has been demonstrated to generalize we...

5 Jingfeng Wu, et al. ∙

∙ 08/11/2021

Gap-Dependent Unsupervised Exploration for Reinforcement Learning

For the problem of task-agnostic reinforcement learning (RL), an agent f...

8 Jingfeng Wu, et al. ∙

∙ 08/10/2021

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...

0 Difan Zou, et al. ∙

∙ 04/17/2021

Lifelong Learning with Sketched Structural Regularization

Preventing catastrophic forgetting while continually learning new tasks ...

9 Haoran Li, et al. ∙

∙ 03/23/2021

Benign Overfitting of Constant-Stepsize SGD for Linear Regression

There is an increasing realization that algorithmic inductive biases are...

9 Difan Zou, et al. ∙

∙ 11/25/2020

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning

In this paper we consider multi-objective reinforcement learning where t...

3 Jingfeng Wu, et al. ∙

∙ 11/04/2020

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate

Understanding the algorithmic regularization effect of stochastic gradie...

14 Jingfeng Wu, et al. ∙

∙ 08/15/2020

Obtaining Adjustable Regularization for Free via Iterate Averaging

Regularization for optimization is a crucial technique to avoid overfitt...

0 Jingfeng Wu, et al. ∙

∙ 06/18/2019

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation

The randomness in Stochastic Gradient Descent (SGD) is considered to pla...

0 Jingfeng Wu, et al. ∙

∙ 08/18/2018

Tangent-Normal Adversarial Regularization for Semi-supervised Learning

The ever-increasing size of modern datasets combined with the difficulty...

0 Bing Yu, et al. ∙

∙ 03/01/2018

The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent

Understanding the generalization of deep learning has raised lots of con...

0 Zhanxing Zhu, et al. ∙

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