[Colloquium] TTI-C Talk: Percy Liang, UC Berkeley
Julia MacGlashan
macglashan at tti-c.org
Wed Apr 22 08:17:29 CDT 2009
REMINDER
When: TODAY, 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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