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AdaME: Adaptive learning of multisource adaptationensembles
Scott Yak · Javier Gonzalvo · Mehryar Mohri · Corinna Cortes

@
in Workshop on Distribution Shifts: Connecting Methods and Applications »
Event URL: https://openreview.net/forum?id=yXOViWIm1Z »

We present a new adaptive algorithm to build multisource domain adaptation neural networks ensembles. Since the standard convex combination ensembles cannot succeed in this scenario, we present a learnable domain-weighted combination and new learning guarantees based on the deep boosting algorithm. We introduce and analyze a new algorithm, ADAME, for this scenario and show that it benefits from favorable theoretical guarantees, is risk-averse and reduces the worst-case mismatch between the inference and training distributions. We also report the results of several experiments demonstrating its performance in the FMOW-WILDSdataset.

Author Information

Scott Yak (Google LLC)
Javier Gonzalvo (Google)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Corinna Cortes (Google Research)

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