Hi All!
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!
Tess
On Apr 13, 2014, at 1:41 PM, Wise, Tess
<wise@fas.harvard.edu<mailto:wise@fas.harvard.edu>> wrote:
Hi All!
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!
Tess
-----------------
Tess Wise
PhD Candidate
Harvard Department of Government
http://tesswise.com<https://urldefense.proofpoint.com/v1/url?u=http://te…
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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