Hyperparameter optimization with REINFORCE and Transformers
This work addresses hyperparameter optimization for machine learning practitioners, offering an incremental improvement with attention-based networks.
The paper tackled hyperparameter optimization by using a simplified Transformer block in a reinforcement learning policy network, achieving improved performance over existing RL methods on NAS-Bench-101 and outperforming Random Search in regression tasks.
Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified Transformer block to model the policy network. The simplified Transformer uses a 2-stream attention-based mechanism to model hyper-parameter dependencies while avoiding layer normalization and position encoding. We posit that this parsimonious design balances model complexity against expressiveness, making it suitable for discovering optimal architectures in high-dimensional search spaces with limited exploration budgets. We demonstrate how the algorithm's performance can be further improved by a) using an actor-critic style algorithm instead of plain vanilla policy gradient and b) ensembling Transformer blocks with shared parameters, each block conditioned on a different auto-regressive factorization order. Our algorithm works well as both a NAS and generic hyper-parameter optimization (HPO) algorithm: it outperformed most algorithms on NAS-Bench-101, a public data-set for benchmarking NAS algorithms. In particular, it outperformed RL based methods that use alternate architectures to model the policy network, underlining the value of using attention-based networks in this setting. As a generic HPO algorithm, it outperformed Random Search in discovering more accurate multi-layer perceptron model architectures across 2 regression tasks. We have adhered to guidelines listed in Lindauer and Hutter while designing experiments and reporting results.