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Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem
We give nearly-tight upper and lower bounds for the improving multi-armed bandits problem.
Applying statistical learning theory to deep learning
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how different architectures may lead to inductive bias when trained using gradient based methods.
Testing Tail Weight of a Distribution Via Hazard Rate
Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data.
Using effective dimension to analyze feature transformations in deep neural networks
In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.
