3 accepted papers
In this paper, we study the stochastic linear bandit problem under the additional requirements of *differential privacy*, *robustness* and *batched observations*. In particular, we assume an adversary randomly chooses a constant fraction of the observed rewards in each batch, replacing them with arb…
We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses. Notably, this setting encompasses several important scenarios that arise in distributed…
Several problems in machine learning, statistics, and other fields rely on computing eigenvectors. For large scale problems, the computation of these eigenvectors is typically performed via iterative schemes such as subspace iteration or Krylov methods. While there is classical and comprehensive ana…