4 research outputs found
Adaptive Estimation of Random Vectors with Bandit Feedback: A mean-squared error viewpoint
We consider the problem of sequentially learning to estimate, in the mean
squared error (MSE) sense, a Gaussian -vector of unknown covariance by
observing only of its entries in each round. We first establish a
concentration bound for MSE estimation. We then frame the estimation problem
with bandit feedback, and propose a variant of the successive elimination
algorithm. We also derive a minimax lower bound to understand the fundamental
limit on the sample complexity of this problem