We consider an online decision making setting known as contextual bandit problem, and
propose an approach for improving contextual bandit performance by using an adaptive …

While learning in an unknown Markov Decision Process (MDP), an agent should trade off
exploration to discover new information about the MDP, and exploitation of the current …

We consider a novel variant of the contextual bandit problem (ie, the multi-armed bandit with
side-information, or context, available to a decision-maker) where the context used at each …

We considered a novel practical problem of online learning with episodically revealed
rewards, motivated by several real-world applications, where the contexts are nonstationary …

The dueling bandits problem has received a lot of attention in recent years due to its
applications in recommendation systems and information retrieval. However, due to the …

We consider a multi-armed bandit setting where, at the beginning of each round, the learner
receives noisy independent, and possibly biased, evaluations of the true reward of each arm …

We formulate a new problem at the intersection of semi-supervised learning and contextual
bandits, motivated by several applications including clinical trials and dialog systems. We …

We consider a novel variant of the contextual bandit problem (ie, the multi-armed bandit with
side-information, or context, available to a decision-maker) where the context used at each …

The rise of personal assistants has made question answering a very popular mechanism for
user-system interaction. In Question Answering System, implicit feedbacks can be easily …

In recent years, machine learning has become an indispensable tool across various industry
domains, revolutionizing the way businesses leverage data to make decisions. One of the …