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Meta Learning in Bandits within Shared Affine Subspaces
We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits.
Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards
In our nonstationary environment, variations in the base arms' distributions, causal relationships between rewards, or both, change the reward generation process.
Hypothesis Transfer in Bandits by Weighted Models
We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning.
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