15 research outputs found
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars
The discovery of neural architectures from simple building blocks is a
long-standing goal of Neural Architecture Search (NAS). Hierarchical search
spaces are a promising step towards this goal but lack a unifying search space
design framework and typically only search over some limited aspect of
architectures. In this work, we introduce a unifying search space design
framework based on context-free grammars that can naturally and compactly
generate expressive hierarchical search spaces that are 100s of orders of
magnitude larger than common spaces from the literature. By enhancing and using
their properties, we effectively enable search over the complete architecture
and can foster regularity. Further, we propose an efficient hierarchical kernel
design for a Bayesian Optimization search strategy to efficiently search over
such huge spaces. We demonstrate the versatility of our search space design
framework and show that our search strategy can be superior to existing NAS
approaches. Code is available at
https://github.com/automl/hierarchical_nas_construction
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluating the performance of computerised AI agents. However, an accurate estimation of the Elo rating (for the top players) often requires many rounds of competitions, which can be expensive to carry out. In this paper, to improve the sample efficiency of the Elo evaluation (for top players), we propose an efficient online match scheduling algorithm. Specifically, we identify and match the top players through a dueling bandits framework and tailor the bandit algorithm to the gradient-based update of Elo. We show that it reduces the per-step memory and time complexity to constant, compared to the traditional likelihood maximization approaches requiring O(t) time. Our algorithm has a regret guarantee of Õ(√T), sublinear in the number of competition rounds and has been extended to the multidimensional Elo ratings for handling intransitive games. We empirically demonstrate that our method achieves superior convergence speed and time efficiency on a variety of gaming tasks