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A theory of learning from different domains
Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a sourceExpand
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Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms
We present GAMUT^1, a suite of game generators designed for testing game-theoretic algorithms. We explain why such a generator is necessary, offer a way of visualizing relationships between the setsExpand
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Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classifier from a sourceExpand
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An optimization-based framework for automated market-making
We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient marketsExpand
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Online Task Assignment in Crowdsourcing Markets
We explore the problem of assigning heterogeneous tasks to workers with different, unknown skill sets in crowdsourcing markets such as Amazon Mechanical Turk. We first formalize the online taskExpand
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Behavioral experiments on biased voting in networks
Many distributed collective decision-making processes must balance diverse individual preferences with a desire for collective unity. We report here on an extensive session of behavioral experimentsExpand
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Adaptive Task Assignment for Crowdsourced Classification
Crowdsourcing markets have gained popularity as a tool for inexpensively collecting data from diverse populations of workers. Classification tasks, in which workers provide labels (such asExpand
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Learning from Multiple Sources
We consider the problem of learning accurate models from multiple sources of "nearby" data. Given distinct samples from multiple data sources and estimates of the dissimilarities between theseExpand
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Efficient Market Making via Convex Optimization, and a Connection to Online Learning
We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient marketsExpand
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Manipulating and Measuring Model Interpretability
Despite a growing body of research focused on creating interpretable machine learning methods, there have been few empirical studies verifying whether interpretable methods achieve their intendedExpand
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