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A Mathematical Framework for Learning Probability Distributions
To demonstrate its efficacy, we present a survey of our results on the approximation error, training error and generalization error of these models, which can all be established based on this framework.
Generalization Error of GAN from the Discriminator's Perspective
The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood.
Generalization and Memorization: The Bias Potential Model
Models for learning probability distributions such as generative models and density estimators behave quite differently from models for learning functions.
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