Self-Regularized Learning Methods
We introduce a general framework for analyzing learning algorithms based on the notion of self-regularization, which captures implicit complexity control without requiring explicit regularization. This is motivated by previous observations that many algorithms, such as gradient-descent based learning, exhibit implicit regularization. In a nutshell, for a self-regularized algorithm the complexity of the predictor is inherently controlled by that of the simplest comparator achieving the same empirical risk. This framework is sufficiently rich to cover both classical regularized empirical risk minimization and gradient descent. Building on self-regularization, we provide a thorough statistic...
| Authors | Max Schölpple, Liu Fanghui, Ingo Steinwart |
|---|---|
| Primary category | stat.ML |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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