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Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where t...
11/25/2020 ∙ by Jingfeng Wu, et al. ∙
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Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate
Understanding the algorithmic regularization effect of stochastic gradie...
11/04/2020 ∙ by Jingfeng Wu, et al. ∙
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Obtaining Adjustable Regularization for Free via Iterate Averaging
Regularization for optimization is a crucial technique to avoid overfitt...
08/15/2020 ∙ by Jingfeng Wu, et al. ∙
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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...
06/18/2019 ∙ by Jingfeng Wu, et al. ∙
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Tangent-Normal Adversarial Regularization for Semi-supervised Learning
The ever-increasing size of modern datasets combined with the difficulty...
08/18/2018 ∙ by Bing Yu, et al. ∙
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The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent
Understanding the generalization of deep learning has raised lots of con...
03/01/2018 ∙ by Zhanxing Zhu, et al. ∙
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