We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to

Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are

These skills are refined and updated through an iterative comparison strategy, enabling efficient adaptation to unseen environments.

Proceedings of the International Conference on Machine Learning 2020