We consider a stochastic contextual bandit problem where the dimension $ d $ of the feature
vectors is potentially large, however, only a sparse subset of features of cardinality $ s_0\ll d …

Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such
as news article recommendation systems, web page ad placement algorithms, and mobile …

We introduce the bilinear bandit problem with low-rank structure in which an action takes the
form of a pair of arms from two different entity types, and the reward is a bilinear function of …

We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave
channels with a dynamic channel support. Millimeter wave beam alignment is challenging …

We consider the stochastic linear contextual bandit problem with high-dimensional features.
We analyze the Thompson sampling algorithm using special classes of sparsity-inducing …

Cluster-of-bandit policy leverages contextual bandits in a collaborative filtering manner and
aids personalized services in the online recommendation system (RecSys). When facing …

We present a data-driven algorithm that advertisers can use to automate their digital ad-
campaigns at online publishers. The algorithm enables the advertiser to search across …

We consider a contextual online learning (multi-armed bandit) problem with high-
dimensional covariate x and decision y. The reward function to learn, f (x, y), does not have a …

We present a novel approach to address the multi-agent sparse contextual linear bandit
problem, in which the feature vectors have a high dimension d whereas the reward function …

High dimensional data analysis has become increasingly frequent and important in diverse
fields of sciences, engineering, genomics, and machine learning (ML), and it has quite …