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Found 6 papers, 0 papers with code
Online Subset Selection using $α$-Core with no Augmented Regret
In this connection, we propose an online learning policy called SCore (Subset Selection with Core) that solves the problem for a large class of reward functions.
$k\texttt{-experts}$ -- Online Policies and Fundamental Limits
Unlike the classic version, where the learner selects exactly one expert from a pool of $N$ experts at each round, in this problem, the learner can select a subset of $k$ experts at each round $(1\leq k\leq N)$.
Dynamic Sample Complexity for Exact Sparse Recovery using Sequential Iterative Hard Thresholding
We prove that if a certain dynamic sample complexity that depends on the sizes of the measurement matrices at each phase, along with their duration and the number of phases, satisfy certain lower bound, the estimation error of SIHT over a fixed time horizon decays rapidly.
Online Caching with Optimal Switching Regret
The objective is to design a caching policy that incurs minimal regret while considering both the rewards due to cache-hits and the switching cost due to the file fetches.
A Two Stage Generalized Block Orthogonal Matching Pursuit (TSGBOMP) Algorithm
Furthermore, assuming real Gaussian sensing matrix entries, we find a lower bound on the probability that the derived recovery bounds are satisfied.
Modified Hard Thresholding Pursuit with Regularization Assisted Support Identification
Hard thresholding pursuit (HTP) is a recently proposed iterative sparse recovery algorithm which is a result of combination of a support selection step from iterated hard thresholding (IHT) and an estimation step from the orthogonal matching pursuit (OMP).
