Title: How Predictors Affect Search Strategies in Neural Architecture Search?. Abstract: Predictor-based Neural Architecture Search (NAS) is an important topic since it can efficiently reduce the computational cost of evaluating candidate architectures. Most existing predictor-based NAS algorithms aim to design different predictors to improve prediction performance. Unfortunately, even a promising performance predictor may suffer from the accuracy decline due to long-term and
continuous usage, thus leading to the degraded performance of the search strategy. That naturally gives rise to the following problems: how predictors affect search strategies and how to appropriately use the predictor? In this paper, we take reinforcement learning (RL) based search strategy to study theoretically and empirically the impact of predictors on search strategies. We first formulate a
predictor-RL-based NAS algorithm as model-based RL and analyze it with a guarantee of monotonic improvement at each trail. Then, based on this analysis, we propose a simple procedure of predictor usage, named mixed batch, which contains ground-truth data and prediction data. The proposed procedure can efficiently reduce the impact of predictor errors on search strategies with maintaining performance growth. Our algorithm, Predictor-based Neural Architecture Search with Mixed batch (PNASM), outperforms traditional NAS algorithms and
prior state-of-the-art predictor-based NAS algorithms on three NAS-Bench-201 tasks.