Arbitrary Order Meta-Learning with Simple Population-Based Evolution
- URL: http://arxiv.org/abs/2303.09478v1
- Date: Thu, 16 Mar 2023 16:55:26 GMT
- Title: Arbitrary Order Meta-Learning with Simple Population-Based Evolution
- Authors: Chris Lu, Sebastian Towers, Jakob Foerster
- Abstract summary: We show that simple population-based evolution implicitly optimises for arbitrarily-high order meta- parameters.
We then introduce a minimal self-referential parameterisation, which in principle enables arbitrary-order meta-learning.
- Score: 1.6114012813668934
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Meta-learning, the notion of learning to learn, enables learning systems to
quickly and flexibly solve new tasks. This usually involves defining a set of
outer-loop meta-parameters that are then used to update a set of inner-loop
parameters. Most meta-learning approaches use complicated and computationally
expensive bi-level optimisation schemes to update these meta-parameters.
Ideally, systems should perform multiple orders of meta-learning, i.e. to learn
to learn to learn and so on, to accelerate their own learning. Unfortunately,
standard meta-learning techniques are often inappropriate for these
higher-order meta-parameters because the meta-optimisation procedure becomes
too complicated or unstable. Inspired by the higher-order meta-learning we
observe in real-world evolution, we show that using simple population-based
evolution implicitly optimises for arbitrarily-high order meta-parameters.
First, we theoretically prove and empirically show that population-based
evolution implicitly optimises meta-parameters of arbitrarily-high order in a
simple setting. We then introduce a minimal self-referential parameterisation,
which in principle enables arbitrary-order meta-learning. Finally, we show that
higher-order meta-learning improves performance on time series forecasting
tasks.
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