O
Omer Tamuz
Researcher at California Institute of Technology
Publications - 158
Citations - 3985
Omer Tamuz is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Probability measure & Planet. The author has an hindex of 31, co-authored 152 publications receiving 3555 citations. Previous affiliations of Omer Tamuz include University of Geneva & Microsoft.
Papers
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Journal ArticleDOI
Correcting systematic effects in a large set of photometric light curves
TL;DR: In this article, a lower-rank approximation of matrices is proposed to remove systematic effects in a large set of lightcurves obtained by a photometric survey, such as atmospheric extinction, detector efficiency, or PSF changes over the detector.
Journal ArticleDOI
Correcting systematic effects in a large set of photometric lightcurves
TL;DR: In this article, a lower-rank approximation of matrices is proposed to remove systematic effects in a large set of lightcurves obtained by a photometric survey, such as atmospheric extinction, detector efficiency, or PSF changes over the detector.
Posted Content
Adaptively Learning the Crowd Kernel
TL;DR: An algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone is introduced, and SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains.
Journal ArticleDOI
Majority dynamics and aggregation of information in social networks
TL;DR: A family of examples in which interaction prevents efficient aggregation of information, and a condition on the social network which ensures that aggregation occurs, is constructed, which shows that if the initial population is sufficiently biased towards a particular alternative then that alternative will eventually become the unanimous preference of the entire population.
Proceedings Article
A Machine Learning Framework for Programming by Example
TL;DR: The authors use machine learning to learn weights that relate textual features describing the provided input-output examples to plausible sub-components of a program, improving search and ranking on a variety of text processing tasks found on help forums.