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- Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
- Machine Learning
- 2009

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 source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work… (More)

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 sets of games supported by GAMUT, and give an overview of GAMUTýs architecture. We highlight the importance of using comprehensive test data by benchmarking… (More)

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 source domain with a large amount of training data to different target domain with very little training data. In this work we give uniform convergence bounds for… (More)

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 markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove that any market satisfying a set of intuitive conditions must price… (More)

- Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan
- Machine Learning
- 2008

We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally believed that active learning does not help, we show that active learning does help in the limit, often with exponential improvements in sample complexity. This contrasts with the traditional analysis of active learning problems… (More)

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 as “offensive” or “not offensive”) for instances (such as “websites”), are among the most common tasks posted, but due to human error and the prevalence of spam, the labels… (More)

- Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
- ACM Trans. Economics and Comput.
- 2013

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 markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove that any market satisfying a set of intuitive conditions must price… (More)

- Chien-Ju Ho, Jennifer Wortman Vaughan
- AAAI
- 2012

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 task assignment problem, in which a requester has a fixed set of tasks and a budget that specifies how many times he would like each task completed. Workers arrive one at… (More)

- Winter A. Mason, Jennifer Wortman Vaughan, Hanna M. Wallach
- Machine Learning
- 2013

Computational social science is an emerging research area at the intersection of computer science, statistics, and the social sciences, in which novel computational methods are used to answer questions about society. The field is inherently collaborative: social scientists provide vital context and insight into pertinent research questions, data sources,… (More)

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 these sources, we provide a general theory of which samples should be used to learn models for each source. This theory is applicable in a broad decision-theoretic learning… (More)