[Colloquium] TTI-C Talk: Percy Liang, UC Berkeley

[Julia MacGlashan]

When:             Wednesday, April 22nd @ 11:00am

Where:            6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor)

Who:               Percy Liang, UC Berkeley

Title:                Asymptotically Optimal Regularization


It is well-known that regularization is crucial for good performance, and in
recent years, machine learning has given rise to a diverse array of
regularizers, especially in semi-supervised learning and multi-task
learning. Which regularizer should one choose?  In this talk, we present a
general method for deriving the asymptotically optimal regularizer for a
given loss function, which provides both insight and quantitative guidance.

Joint work with Francis Bach, Guillaume Bouchard, and Michael Jordan

Bio: 
Percy Liang is a fourth-year Ph.D. student at UC Berkeley working with
Michael Jordan and Dan Klein.  He graduated with a bachelors in computer
science and math from MIT.  He works actively in both natural language
processing and machine learning, focusing on unsupervised learning of rich
latent-variable probabilistic models and also developing theoretical
analyses.  He holds NDSEG and NSF fellowships and has received a best
student paper award at ICML in 2008.  He is also an aspiring concert
pianist.
Contact:          Nati Srebro, TTI-C	   nati at tti-c.org	834-7493



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