Adaptive and sequential experiment design is a well-studied area in numerous domains. We
survey and synthesize the work of the online statistical learning paradigm referred to as multi …

We study how to learn optimal interventions sequentially given causal information
represented as a causal graph along with associated conditional distributions. Causal …

We consider a variant of the multi-armed bandit model, which we call multi-armed bandit
problem with known trend, where the gambler knows the shape of the reward function of …

This work presents an extension of Thompson Sampling bandit policy for orchestrating the
collection of base recommendation algorithms for e-commerce. We focus on the problem of …

Despite the subject of non-stationary bandit learning having attracted much recent attention,
we have yet to identify a formal definition of non-stationarity that can consistently distinguish …

Insufficient knowledge about a new bug or a new developer, in the context of
recommendations done in software bug repositories (SBR) mining, impacts the …

Rule-based systems have been used increasingly to augment learning algorithms for
annotating data. Rules alleviate many of the shortcomings inherent in pure algorithmic …

Sleep is an essential activity that affects an individual's health and ability to perform activities
of daily living (ADL). Inadequate sleep reduces cognitive capacity and leads to health …

Real-world applications of contextual bandits often exhibit non-stationarity due to
seasonality, serendipity, and evolving social trends. While a number of non-stationary …

Recommender systems have become an integral part of virtually every e-commerce
application on the web. The deployment of these expert systems has enabled users to …