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A support vector method for optimizing average precision
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 precisionExpand
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Interactively optimizing information retrieval systems as a dueling bandits problem
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 requireExpand
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The K-armed Dueling Bandits Problem
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 approachesExpand
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Beat the Mean Bandit
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 absoluteExpand
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Large-scale validation and analysis of interleaved search evaluation
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 techniqueExpand
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Predicting diverse subsets using structural SVMs
In many retrieval tasks, one important goal involves retrieving a diverse set of results (e.g., documents covering a wide range of topics for a search query). First of all, this reduces redundancy,Expand
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Linear Submodular Bandits and their Application to Diversified Retrieval
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 isExpand
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Multi-Level Structured Models for Document-Level Sentiment Classification
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 knownExpand
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Predicting structured objects with support vector machines
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 ariseExpand
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An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment
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 levelExpand
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