Dean P. Foster
Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation.
Tue, 05/01/2012 - 15:01 — SomNambulAuthors: Animashree Anandkumar, Sham M. Kakade, Daniel Hsu, Dean P. Foster, Yi-Kai Liu
Subjects: Learning
AbstractTopic models can be seen as a generalization of the clustering problem, in
that they posit that observations are generated due to multiple latent factors
(e.g. the words in each document are generated as a mixture of several active
topics, as opposed to just one). This increased representational power comes at
the cost of a more challenging unsupervised learning problem of estimating the
topic probability vectors (the distributions over words for each topic), when
only the words are observed and the corresponding topics are hidden.The effect of winning an Oscar Award on survival: Correcting for healthy performer survivor bias with a rank preserving structural accelerated failure time model.
Thu, 08/04/2011 - 15:01 — SomNambulAuthors: Dylan S. Small, Xu Han, Dean P. Foster, Vishal Patel
Subjects: Applications
AbstractWe study the causal effect of winning an Oscar Award on an actor or actress's
survival. Does the increase in social rank from a performer winning an Oscar
increase the performer's life expectancy? Previous studies of this issue have
suffered from healthy performer survivor bias, that is, candidates who are
healthier will be able to act in more films and have more chance to win Oscar
Awards. To correct this bias, we adapt Robins' rank preserving structural
accelerated failure time model and $g$-estimation method.Stochastic convex optimization with bandit feedback.
Tue, 07/12/2011 - 15:01 — SomNambulAuthors: Sham M. Kakade, Daniel Hsu, Alekh Agarwal, Alexander Rakhlin, Dean P. Foster
Subjects: Optimization and Control
AbstractThis paper addresses the problem of minimizing a convex, Lipschitz function
$f$ over a convex, compact set $\xset$ under a stochastic bandit feedback
model. In this model, the algorithm is allowed to observed noisy realizations
of the function value $f(x)$ at any query point $x \in \xset$. The quantity of
interest is regret of the algorithm, which is the sum of the function values at
algorithm's query points minus the optimal function value. We demonstrate a
generalization of the ellipsoid algorithm that incurs $\otil(\poly(d)\sqrt{T})$
regret.
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