Online Learning in Contextual Bandits using Gated Linear Networks

NeurIPS 2020

Online Learning in Contextual Bandits using Gated Linear Networks

Dec 06, 2020
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We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recently introduced deep learning architecture with properties well-suited to the online setting. Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead. We empirically evaluate GLCB compared to 9 state-of-the-art algorithms that leverage deep neural networks, on a standard benchmark suite of discrete and continuous contextual bandit problems. GLCB obtains median first-place despite being the only online method, and we further support these results with a theoretical study of its convergence properties. Speakers: Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness

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