Information Complexity and Generalization Bounds
This work provides incremental theoretical insights into generalization bounds, benefiting researchers in statistical learning theory by offering a unified framework and extensions to existing methods.
The paper tackles the problem of deriving generalization bounds for randomized learning algorithms by unifying PAC-Bayesian and mutual information-based approaches using Tong Zhang's information exponential inequality, resulting in new bounds for data-dependent priors and unbounded loss functions, with practical examples like Entropy- and PAC-Bayes-SGD for neural networks.
We present a unifying picture of PAC-Bayesian and mutual information-based upper bounds on the generalization error of randomized learning algorithms. As we show, Tong Zhang's information exponential inequality (IEI) gives a general recipe for constructing bounds of both flavors. We show that several important results in the literature can be obtained as simple corollaries of the IEI under different assumptions on the loss function. Moreover, we obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing the bounds gives rise to variants of the Gibbs algorithm, for which we discuss two practical examples for learning with neural networks, namely, Entropy- and PAC-Bayes- SGD. Further, we use an Occam's factor argument to show a PAC-Bayesian bound that incorporates second-order curvature information of the training loss.