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To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on(More)
A promising architecture for content caching in wireless small cell networks is storing popular files at small base stations (sBSs) with limited storage capacities. Using localized communication, an sBS serves local user requests, while reducing the load on the macro cellular network. The sBS should cache the most popular files to maximize the number of(More)
In this paper, we propose an expert selection system that learns online the best expert to assign to each patient depending on the context of the patient. In general, the context can include an enormous number and variety of information related to the patient's health condition, age, gender, previous drug doses, and so forth, but the most relevant(More)
Standard Multi-Armed Bandit (MAB) problems assume that the arms are independent. However, in many application scenarios, the information obtained by playing an arm provides information about the remainder of the arms. Hence, in such applications, this in-formativeness can and should be exploited to enable faster convergence to the optimal solution. In this(More)
Video-based object or face recognition services on mobile devices have recently garnered significant attention, given that video cameras are now ubiquitous in all mobile communication devices. In one of the most typical scenarios for such services, each mobile device captures and transmits video frames over wireless to a remote computing cluster (a.k.a.(More)
Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance.(More)
To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the(More)
Hospitals are increasingly utilizing business intelligence and analytics tools to mine electronic health data to uncover inefficiencies in care delivery (e.g., slow turnaround times, high readmission rates). Given that the expertise and experience of healthcare providers may vary significantly, an area of potential improvement is optimizing the way patient(More)
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information , is biased and possesses many features. The approach is applicable in a wide variety of settings from healthcare to advertising to education to finance. These settings have in common that the decision maker can(More)
Standard multi-armed bandits model decision problems in which the consequences of each action choice are unknown and independent of each other. But in a wide variety of decision problems – from drug dosage to dynamic pricing – the consequences (rewards) of different actions are correlated, so that selecting one action provides information about the(More)