[AI Seminar] AI Lunch - Pengtao Xie - February 16th, 2016

Ellen Vitercik vitercik at cs.cmu.edu
Wed Feb 10 19:04:44 EST 2016

Dear faculty and students,

We look forward to seeing you this Tuesday, February 16th, at noon in NSH
3305 for AI lunch <http://www.cs.cmu.edu/~aiseminar/>. Pengtao Xie
<http://www.cs.cmu.edu/~pengtaox/> will give a talk titled
"Diversity-Inducing Learning of Latent Variable Models."

*Abstract:* One central task in machine learning (ML) is to extract
underlying patterns, structure and knowledge from data. Latent variable
models (LVMs) are principled and effective tools to achieve this goal. Due
to the dramatic growth of volume and complexity of big data, several new
challenges have emerged and cannot be effectively addressed by existing
LVMs: (1) How to capture long-tail patterns that carry crucial information
when the popularity of patterns is distributed in a power-law fashion? (2)
How to reduce model complexity and computational cost without compromising
the modeling power of LVMs? (3) How to improve the interpretability and
reduce the redundancy of discovered patterns? To addresses the three
challenges, we develop a novel regularization technique for LVMs, which
controls the geometry of the latent space during learning to enable the
learned latent components of LVMs to be diverse, to accomplish long-tail
coverage, low redundancy, and better interpretability. In this talk, I will
introduce: 1) how the diversity-inducing mutual angular regularizer (MAR)
is defined; 2) how to optimize the MAR which is non-convex and non-smooth;
3) a theoretical analysis of why MAR is effective; 4) the applications of
MAR in representation learning and distance metric learning.


Ellen and Ariel
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