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We introduce a new and completely online contextual bandit algorithm calledGated Linear Contextual Bandits (GLCB). This algorithm is based on Gated LinearNetworks (GLNs), a recently introduced deep learning architecture withproperties well-suited to the online setting. Leveraging data-dependent gatingproperties of the GLN we are able to estimate prediction uncertainty witheffectively zero algorithmic overhead. We empirically evaluate GLCB compared to9 state-of-the-art algorithms that leverage deep neural networks, on a standardbenchmark suite of discrete and continuous contextual bandit problems. GLCBobtains median first-place despite being the only online method, and we furthersupport these results with a theoretical study of its convergence properties.

Online Learning in Contextual Bandits using Gated Linear Networks