2015

Tamara Broderick (MIT) - Feature allocations, probability functions, and paintboxes Wednesday, February 25, 2015

Abstract: Clustering involves placing entities into mutually exclusive
categories. We wish to relax the requirement of mutual exclusivity,
allowing objects to belong simultaneously to multiple classes, a
formulation that we refer to as "feature allocation." The first step
is a theoretical one. In the case of clustering the class of
probability distributions over exchangeable partitions of a dataset
has been characterized (via exchangeable partition probability...

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Justin Reich (Harvard) - Massive Open Online Courses and the Science of Learning Wednesday, February 18, 2015
Abstract: Large-scale open online learning environments continuously record learner activities: the 54 courses conducted by HarvardX and MITx in the 2013-2014 academic year had 1.1MM participants who recorded over a half a billion actions. Increasingly, online learning platforms also support A/B testing frameworks that allow for a variety of experimental designs. This combination of data recording and experimentation opens up excited new avenues for educational research. This talk will provide an overview of the various...
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Kayhan Batmanghelich (MIT) - Joint Modeling Imaging and Genetics: a Probabilistic Approach Wednesday, February 11, 2015

Abstract: An increasing number of clinical and imaging research studies is collecting various additional information including genetic data. The goals of the emerging field of imaging genetics can be summarized into two aims: 1) using imaging biomarkers as an intermediate phenotype to uncover underlying biological mechanisms of diseases; 2) phenotype discovery. 
In this talk, we will focus on the first goal, namely using imaging as an intermediate phenotype, and briefly discuss the...
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Jelani Nelson (Harvard) - Dimensionality Reduction Via Sparse Matrices Wednesday, January 28, 2015

Abstract- This talk will discuss sparse Johnson-Lindenstrauss transforms, i.e.sparse linear maps into much lower dimension which preserve the Euclidean geometry of a set of vectors. Both upper and lower bounds will be presented, as well as applications to certain domains such as numerical linear algebra and compressed sensing.  Based on various joint works with Jean Bourgain, Daniel M. Kane, and Huy Le Nguyen.

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