Towards an Empirical Theory of Deep Learning

Abstract

In this thesis, we take an empirical approach to the theory of deep learning. We treat deep learning systems as black boxes, with inputs we can control (train samples, architecture, model size, optimizer, etc.) and outputs we can observe (the neural network function, its test error, its parameters, etc.). Our goal is to characterize how our choice of inputs affects the outputs. As an empirical theory, we aim to describe this behavior quantitatively, if not prove it rigorously. We hope for theories that are as general and universal as possible, applying in a wide range of deep learning settings, including those in practice.

We present three empirical theories towards this goal. (1) Deep Double Descent demonstrates that the relationship between inputs and outputs in deep learning is not always monotonic in natural ways: there is a predictable "critical regime" where, for example, training on more data can actually hurt performance, but models are well-behaved outside this regime. (2) The Deep Bootstrap Framework shows that to understand the *generalization* of the output network, it is sufficient to understand *optimization* aspects of our input choices. (3) Distributional Generalization takes a closer look at the output network, and finds that trained models actually "generalize" in a much broader sense than we classically expect. We introduce a new kind of generalization to capture these behaviors.

Our results shed light on existing topics in learning theory (especially generalization, overparameterization, interpolation), and also reveal new phenomena which require new frameworks to capture. In some cases, our study of deep learning has exposed phenomena that hold even for non-deep methods. We thus hope the results of this thesis will eventually weave into a general theory of learning, deep and otherwise.