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Optimisic Information Directed Sampling
We study the problem of online learning in contextual bandit problems where the loss function is assumed to belong to a known parametric function class.
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts.
Efficient conditioned face animation using frontally-viewed embedding
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.
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