[gov3009-l] Applied Stats Today (4/16): Finale Doshi
wise at fas.harvard.edu
Wed Apr 16 07:24:41 EDT 2014
Just a reminder that our speaker today will be Finale Doshi, speaking about Prediction and Interpretation with Latent Variable Models.
Hope to see you all at the talk!
On Apr 13, 2014, at 1:41 PM, Wise, Tess <wise at fas.harvard.edu<mailto:wise at fas.harvard.edu>> wrote:
Our speaker this Wednesday (4/16) at Applied Stats will be Finale Doshi, a post-doc at Harvard Medical School and the Harvard School of Engineering and Applied Sciences. Finale completed her PhD in Computer Science from MIT in 2012 which applied Bayesian nonparametric models (which have the nice property of scaling the sophistication of learned models with the complexity of the data) to problems in reinforcement learning.
Finale will be giving a talk entitled Prediction and Interpretation with Latent Variable Models. The abstract for the talk is included below. As per usual, we will meet in CGIS K354 at 12 noon and lunch will be served.
I look forward to seeing you all there!
Harvard Department of Government
Prediction and Interpretation with Latent Variable Models
Latent variable models provide a powerful tool for summarizing data through a set of hidden variables. These models are generally trained to maximize prediction accuracy, and modern latent variable models now do an excellent job of finding compact summaries of the data with high predictive power. However, there are many situations in which good predictions alone are not sufficient. Whether the hidden variables have inherent value by providing insights about the data, or whether we simply wish to improve a system, understanding what the discovered hidden variables mean is an important first step.
In this talk, I will discuss one particular model, GraphSparse LDA, for discovering interpretable latent structures without sacrificing (and sometimes improving upon) prediction accuracy. The model incorporates knowledge about the relationships between observed dimensions into a probabilistic framework to find a small set of human-interpretable "concepts" that summarize the observed data. This approach allows us to recover interpretable descriptions of clincially-relevant autism phenotypes from a medical dataset with thousands of dimensions.
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