TOFU-POV recovers a latent action subspace from randomly masked features and achieves √T regret scaling with intrinsic dimension m rather than ambient dimension d.
High-dimensional covariance matrix estimation with missing observations
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Stochastic Linear Bandits with Partially Observed Actions
TOFU-POV recovers a latent action subspace from randomly masked features and achieves √T regret scaling with intrinsic dimension m rather than ambient dimension d.