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ChaCha for Online AutoML

Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi

[ Abstract ] [ Livestream: Visit Online Learning 1 ] [ Paper ]

We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added into consideration by an application-dependent oracle based on the champions. Empirically, we show that ChaCha provides good performance across a wide array of datasets when optimizing over featurization and hyperparameter decisions.

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