Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
This addresses covariate shift in machine learning for practitioners dealing with multiple training sources, but it is incremental as it builds on existing SGD and tree-search methods.
The paper tackles the covariate shift problem where multiple training datasets with unobserved features exist, aiming to find the optimal mixture distribution for best validation performance. The result is the Mix&Match algorithm, which combines SGD with optimistic tree search and model re-use, proven to have simple regret guarantees and validated on two real-world datasets.
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions. This covariate shift is caused, in part, due to unobserved features in the datasets. The objective, then, is to find the best mixture distribution over the training datasets (with only observed features) such that training a learning algorithm using this mixture has the best validation performance. Our proposed algorithm, ${\sf Mix\&Match}$, combines stochastic gradient descent (SGD) with optimistic tree search and model re-use (evolving partially trained models with samples from different mixture distributions) over the space of mixtures, for this task. We prove simple regret guarantees for our algorithm with respect to recovering the optimal mixture, given a total budget of SGD evaluations. Finally, we validate our algorithm on two real-world datasets.