  Slow concept drift is a ubiquitous, yet under-studied problem in practical
machine learning systems. In such settings, although recent data is more
indicative of future data, naively prioritizing recent instances runs the risk
of losing valuable information from the past. We propose an optimization-driven
approach towards balancing instance importance over large training windows.
First, we model instance relevance using a mixture of multiple timescales of
decay, allowing us to capture rich temporal trends. Second, we learn an
auxiliary scorer model that recovers the appropriate mixture of timescales as a
function of the instance itself. Finally, we propose a nested optimization
objective for learning the scorer, by which it maximizes forward transfer for
the learned model. Experiments on a large real-world dataset of 39M photos over
a 9 year period show upto 15% relative gains in accuracy compared to other
robust learning baselines. We replicate our gains on two collections of
real-world datasets for non-stationary learning, and extend our work to
continual learning settings where, too, we beat SOTA methods by large margins.
