Google Research: instance-conditional timescales of decay to reweight training data under concept drift
Google Research proposes learning an auxiliary, instance-conditional weighting model that assigns importance scores to training examples as a function of their content and age, combining multiple fixed timescales of decay and meta-learning the assignment alongside the primary model. The method aims to blend benefits of offline and continual learning and yields up to ~15% relative accuracy gains on a large nonstationary photo benchmark (~39M images over 10 years) and improvements across other nonstationary learning benchmarks.