End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
- URL: http://arxiv.org/abs/2305.15930v4
- Date: Fri, 22 Dec 2023 09:38:26 GMT
- Title: End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
- Authors: Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou Ammar
- Abstract summary: This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures.
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
- Score: 52.818579746354665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of
Bayesian optimisation by leveraging data from related tasks. While previous
methods successfully meta-learn either a surrogate model or an acquisition
function independently, joint training of both components remains an open
challenge. This paper proposes the first end-to-end differentiable meta-BO
framework that generalises neural processes to learn acquisition functions via
transformer architectures. We enable this end-to-end framework with
reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Early on, we notice that training transformer-based neural processes from
scratch with RL is challenging due to insufficient supervision, especially when
rewards are sparse. We formalise this claim with a combinatorial analysis
showing that the widely used notion of regret as a reward signal exhibits a
logarithmic sparsity pattern in trajectory lengths. To tackle this problem, we
augment the RL objective with an auxiliary task that guides part of the
architecture to learn a valid probabilistic model as an inductive bias. We
demonstrate that our method achieves state-of-the-art regret results against
various baselines in experiments on standard hyperparameter optimisation tasks
and also outperforms others in the real-world problems of mixed-integer
programming tuning, antibody design, and logic synthesis for electronic design
automation.
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