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<span id="m_-3707508104918808491m_1056074707391118660m_7412031204206922646gmail-m_5065654319805229716gmail-m_-3904694201176032715gmail-m_7292068599179452778m_4747140382883879361gmail-m_-4714659723393895497m_-5447680322720688808gmail-m_-6673895060665256028m_3778844496755075432gmail-m_5749055011804307575m_3316997134704725064m_7082043948795332671gmail-m_3918681043343225718m_7776154040874388940gmail-docs-internal-guid-d34d626a-7fff-fa18-31b7-15dad8158c9a"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> Monday</font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">, March 9th @ 11:00am
</font></font></font></p></span><div class="gmail_quote"><div dir="ltr"><div><div class="gmail_quote"><br><span id="m_-3707508104918808491m_1056074707391118660m_7412031204206922646gmail-m_5065654319805229716gmail-m_-3904694201176032715gmail-m_7292068599179452778m_4747140382883879361gmail-m_-4714659723393895497m_-5447680322720688808gmail-m_-6673895060665256028m_3778844496755075432gmail-m_5749055011804307575m_3316997134704725064m_7082043948795332671gmail-m_3918681043343225718m_7776154040874388940gmail-docs-internal-guid-d34d626a-7fff-fa18-31b7-15dad8158c9a"></span><div dir="ltr"><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</font></font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> Mina Karzand, U of W, Madison</font></font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><br></font></font></font></p><b>Title: </b>
<font size="2">Focused learning in Graphical models</font>
</div></div></div></div></div>
<div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><b><font face="arial, helvetica, sans-serif"><br></font></b></div><div dir="ltr"><b><font face="arial, helvetica, sans-serif">
</font></b><div><font size="2"><b>Abstract:</b> When there is insufficient data to learn a globally accurate model,
successful learning can still be possible if we take into account the
particular task for which the learned model will be employed. Learning a
possibly incorrect or incomplete model, which
performs well in the subsequent prediction (classification or decision
making) tasks requires far fewer training examples than learning a
complete model. My work instantiates this aspiration in several rich and
complex data-driven systems including learning
graphical models, online collaborative filtering, and active
learning. In this talk, I dive into the problem of learning graphical
models with this framework in mind.<br>
<br>
In the first half of the talk, I look into learning tree-structured
Ising models in which the learned model is used subsequently for
prediction based on partial observations (given the realization of a
subset of variables, predict the value of the remaining
ones). The vast majority of previous work on learning graphical models
aims to correctly recover the underlying graph structure (an impossible
task in the data-constrained regime). I show that it is possible to
efficiently learn a tree model that gives accurate
predictions even when there is insufficient data to learn the correct
structure.<br>
<br>
The second half of the talk is about speciation rate estimation in
phylogenetic trees. This problem is essentially one of inferring
features of the model (in this case, the speciation or extinction rate)
from partial observations (the sequences at the leaves
of the tree) of a latent tree model (phylogeny). I show that an
incomplete and partially incorrect summary of the tree</font><font size="2"> structure is enough to estimate the speciation rate with the minimax optimal dependence on the length of observed
DNA sequences.</font></div><div><font size="2"><br></font></div><div><font size="2"><br></font></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
<div><div dir="ltr" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif"><b>Jerome Allen</b><br></font><div><font face="arial, helvetica, sans-serif">Executive Assistant to the President<br></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 518</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></div><div><font face="arial, helvetica, sans-serif">p:(773) 702-2311<br></font></div><div><i><b><a href="mailto:jallen@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">jallen@ttic.edu</font></a></b></i></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>