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Poster
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou · Lihong Li · Quanquan Gu

Tue Jul 14 09:00 AM -- 09:45 AM & Tue Jul 14 10:00 PM -- 10:45 PM (PDT) @ Virtual
in Poster Session 3 »
We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other than boundedness. We propose a new algorithm, NeuralUCB, which leverages the representation power of deep neural networks and uses a neural network-based random feature mapping to construct an upper confidence bound (UCB) of reward for efficient exploration. We prove that, under standard assumptions, NeuralUCB achieves $\tilde O(\sqrt{T})$ regret, where $T$ is the number of rounds. To the best of our knowledge, it is the first neural network-based contextual bandit algorithm with a near-optimal regret guarantee. We also show the algorithm is empirically competitive against representative baselines in a number of benchmarks.
Keywords: Deep Learning Theory · Online Learning / Bandits · Online Learning, Active Learning, and Bandits
[ Slides] 

Author Information

Dongruo Zhou (UCLA)
Lihong Li (Google Research)
Quanquan Gu (University of California, Los Angeles)

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