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Poster
Online Learning in Contextual Bandits using Gated Linear Networks
Eren Sezener · Marcus Hutter · David Budden · Jianan Wang · Joel Veness

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #592

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 mean first-place despite being the only online method, and we further support these results with a theoretical study of its convergence properties.

Author Information

Eren Sezener (DeepMind)
Marcus Hutter (DeepMind)
David Budden (DeepMind)
Jianan Wang (DeepMind)
Joel Veness (Deepmind)

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