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A support vector method for optimizing average precision
TLDR
This work presents a general SVM learning algorithm that efficiently finds a globally optimal solution to a straightforward relaxation of MAP, and shows its method to produce statistically significant improvements in MAP scores. Expand
Interactively optimizing information retrieval systems as a dueling bandits problem
TLDR
An on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems and an algorithm with theoretical guarantees as well as simulation results are presented. Expand
The K-armed Dueling Bandits Problem
TLDR
A novel regret formulation is proposed in this setting, as well as an algorithm that achieves information-theoretically optimal regret bounds (up to a constant factor) is presented. Expand
Beat the Mean Bandit
TLDR
This paper presents the first algorithm for this more general Dueling Bandits Problem and provides theoretical guarantees in both the online and the PAC settings and shows that the new algorithm has stronger guarantees than existing results even in the original DuelingBandits Problem, which is validated empirically. Expand
Large-scale validation and analysis of interleaved search evaluation
TLDR
This paper provides a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature, and analyzes the agreement ofinterleaving with manual relevance judgments and observational implicit feedback measures. Expand
Batch Policy Learning under Constraints
TLDR
A new and simple method for off-policy policy evaluation (OPE) and derive PAC-style bounds is proposed and achieves strong empirical results in different domains, including in a challenging problem of simulated car driving subject to multiple constraints such as lane keeping and smooth driving. Expand
Predicting diverse subsets using structural SVMs
TLDR
This work formulate the learning problem of predicting diverse subsets and derive a training method based on structural SVMs that explicitly trains to diversify results. Expand
Linear Submodular Bandits and their Application to Diversified Retrieval
TLDR
This paper proposes 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, and presents an algorithm, called LSBGREEDY, and proves that it efficiently converges to a near-optimal model. Expand
Multi-Level Structured Models for Document-Level Sentiment Classification
TLDR
A joint two-level approach for document-level sentiment classification that simultaneously extracts useful (i.e., subjective) sentences and predicts document- level sentiment based on the extracted sentences is proposed. Expand
Predicting structured objects with support vector machines
TLDR
The following explores a generalization of Support Vector Machines for complex prediction problems in natural language processing, search engines, and bioinformatics. Expand
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