Incremental Learning-to-Learn with Statistical Guarantees

Understand

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution.

• In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks.
• Key to this setting is for the algorithm to rapidly incorporate new observations into the model as they arrive, without keeping them in memory.
• We focus on the case where the underlying algorithm is ridge regression parameterized by a positive semidefinite matrix.