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Covariate Distribution Aware Meta-learning | | Amrith Setlur
; Saket Dingliwal
; Barnabas Poczos
; | | Date: |
6 Jul 2020 | | Abstract: | Meta-learning has proven to be successful at few-shot learning across the
regression, classification and reinforcement learning paradigms. Recent
approaches have adopted Bayesian interpretations to improve gradient based
meta-learners by quantifying the uncertainty of the post-adaptation estimates.
Most of these works almost completely ignore the latent relationship between
the covariate distribution (p(x)) of a task and the corresponding conditional
distribution p(y|x). In this paper, we identify the need to explicitly model
the meta-distribution over the task covariates in a hierarchical Bayesian
framework. We begin by introducing a graphical model that explicitly leverages
very few samples drawn from p(x) to better infer the posterior over the optimal
parameters of the conditional distribution (p(y|x)) for each task. Based on
this model we provide an inference strategy and a corresponding meta-algorithm
that explicitly accounts for the meta-distribution over task covariates.
Finally, we demonstrate the significant gains of our proposed algorithm on a
synthetic regression dataset. | | Source: | arXiv, 2007.2523 | | Services: | Forum | Review | PDF | Favorites | |
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