Theory¶

Focus on generalization¶

Generalization error is what we really want to minimize when we train a model. Most algorithms minimize generalization error indirectly by minimizing a loss function that consists of a training loss term and additional penalty terms to discourage the models away from acquiring properties that are associated with overfitting (e.g., L1 weight norms, L2 weight norms).

Rigorous trade-offs between training loss and complexity¶

How do we know what model properties to avoid? Currently, these usually come from practical experience or industry-accepted best practices. While this has worked well so far, we would like to minimize the generalization error in a more principled way.

AdaNet’s approach is to minimize a theoretical upper bound on generalization error, proven in the DeepBoost paper [Cortes et al. ‘14]:

$$R(f) \leq \widehat{R}{S, \rho}(f) + \frac{4}{\rho} \sum{k = 1}^{l} \big | \mathbf{w} _k \big |_1 \mathfrak{R}_m(\widetilde {\cal H}_k) + \widetilde O\Big(\frac{1}{\rho} \sqrt{\frac{\log l}{m}}\Big)$$

This generalization bound allows us to make an apples-to-apples comparison between the complexities of models in an ensemble and the overall training loss – allowing us to design an algorithm that makes this trade-off in a rigorous manner.

Other key insights¶

• Convex combinations can’t hurt. Given a set of already-performant and uncorrelated base learners, one can take a linear combination of them with weights that sum to 1 to obtain an ensemble that outperforms the best among those base learners. But even though this ensemble has more trainable parameters, it does not have a greater tendency to overfit.
• De-emphasize rather than discourage complex models. If one combines a few base learners that are each selected from a different function class (e.g., neural networks of different depths and widths), one might expect the tendency to overfit to be similar to that of an ensemble comprised of base learners selected from the union of all the function classes. Remarkably, the DeepBoost bound shows that we can actually do better, as long as the final ensemble is a weighted average of model logits where each base learner’s weight is inversely proportional to the Rademacher complexity of its function class, and all the weights in the logits layer sum to 1. Additionally, at training time, we don’t have to discourage the trainer from learning complex models – it is only when we consider the how much the model should contribute to the ensemble do we take the complexity of the model into account.
• Complexity is not just about the weights. The Rademacher complexity of a neural network does not simply depend on the number of weights or the norm of its weights – it also depends on the number of layers and how they are connected. An upper bound on the Rademacher complexity of neural networks can be expressed recursively [Cortes et al. ‘17], and applies to both fully-connected and convolutional neural networks, thus allowing us to compute the complexity upper-bounds of almost any neural network that can be expressed as a directed-acyclic graph of layers, including unconventional architectures such as those found by NASNet [Zoph et al. ‘17]. Rademacher complexity is also data-dependent, which means that the same neural network architecture can have different generalization behavior on different data sets.

\begin{align*} &F\left ( w \right ) = \frac{1}{m} \sum_{i=1}^{m} \Phi \left (\sum_{j=1}^{N}w_jh_j(x_i), y_i \right ) + \sum_{j=1}^{N} \left (\lambda r(h_j) + \beta \right )\left | w_j \right |\ &\text{where }w_j \text{ is the weight of model } j \text{‘s contribution to the ensemble,}\ &h_j \text{ is model } j,\ &\Phi \text{ is the loss function,}\ &r(h_j) \text{ is model } j\text{‘s complexity, and}\ &\lambda \text{ and } \beta \text{ are tunable hyperparameters.} \end{align*}