2 citations · 2 across the 2 of their papers we have counts for
6 papers
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Difan Zou, Jingfeng Wu, Vladimir Braverman +2
Stochastic gradient descent (SGD) has achieved great success due to its superior performance in both optimization and generalization. Most of existing generalization analyses are m…
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman +2
There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparamet…
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences. In practice, the preferences are often given in an adversarial…
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Regularization for optimization is a crucial technique to avoid overfitting in machine learning. In order to obtain the best performance, we usually train a model by tuning the reg…
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
Bing Yu, Jingfeng Wu, Jinwen Ma +1
Compared with standard supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. A recently proposed method, virtual advers…
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Zhanxing Zhu, Jingfeng Wu, Bing Yu +2
Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we study a general for…