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Machine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for mean average precision (MAP), despite its widespread use in evaluating such systems. Existing approaches optimizing MAP either do not find a globally optimal solution, or are(More)
a r t i c l e i n f o a b s t r a c t We study a partial-information online-learning problem where actions are restricted to noisy comparisons between pairs of strategies (also known as bandits). In contrast to conventional approaches that require the absolute reward of the chosen strategy to be quantifiable and observable, our setting assumes only that(More)
We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm(More)
Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture of its efficiency and effectiveness is still lacking. In(More)
In this paper, we investigate structured models for document-level sentiment classification. When predicting the sentiment of a subjective document (e.g., as positive or negative), it is well known that not all sentences are equally discriminative or informative. But identifying the useful sentences automatically is itself a difficult learning problem. This(More)
The Dueling Bandits Problem is an online learning framework in which actions are restricted to noisy comparisons between pairs of strategies (also called bandits). It models settings where absolute rewards are difficult to elicit but pairwise preferences are readily available. In this paper, we extend the Dueling Bandits Problem to a relaxed setting where(More)
Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear sub-modular bandits problem, which is an online learning setting for optimizing a general class of feature-rich submodular utility models for diversified retrieval. We present an algorithm, called(More)
We present an efficient approach to ambulance fleet allocation and dynamic redeployment, where the goal is to position an entire fleet of ambulances to base locations to maximize the service level (or utility) of the Emergency Medical Services (EMS) system. We take a simulation-based approach, where the utility of an allocation is measured by directly(More)
Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for(More)