Timezone: »

 
Poster
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw · Rajat Sen · Karthikeyan Shanmugam · Constantine Caramanis · Sanjay Shakkottai

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1002
in Poster Session 4 »

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. The distribution shift is due, in part, to \emph{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, \textsf{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 a novel high probability bound on the final SGD iterate without relying on a global gradient norm bound, and use it to show the advantages of model re-use. Additionally, we provide 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.

Author Information

Matthew Faw (University of Texas at Austin)
Rajat Sen (Google)
Karthikeyan Shanmugam (IBM Research, NY)
Constantine Caramanis (UT Austin)
Sanjay Shakkottai (University of Texas at Austin)

More from the Same Authors