Covariate Distribution Aware Meta-learning
- URL: http://arxiv.org/abs/2007.02523v3
- Date: Sat, 28 Nov 2020 02:07:27 GMT
- Title: Covariate Distribution Aware Meta-learning
- Authors: Amrith Setlur, Saket Dingliwal, Barnabas Poczos
- Abstract summary: We propose a computationally feasible meta-learning algorithm by introducing meaningful relaxations.
We demonstrate the gains of our algorithm over bootstrapped based meta-learning baselines on popular classification benchmarks.
- Score: 3.494950334697974
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Meta-learning has proven to be successful for 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 leverages the samples
from the marginal $p(x)$ to better infer the posterior over the optimal
parameters of the conditional distribution $(p(y|x))$ for each task. Based on
this model we propose a computationally feasible meta-learning algorithm by
introducing meaningful relaxations in our final objective. We demonstrate the
gains of our algorithm over initialization based meta-learning baselines on
popular classification benchmarks. Finally, to understand the potential benefit
of modeling task covariates we further evaluate our method on a synthetic
regression dataset.
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