[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Adam Kalai, Monday Nov 7, 12:30pm- reminder

Barbara Engelhardt bee at princeton.edu
Sun Nov 6 19:51:05 EST 2016

Talk of interest on Monday.


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Colloquium Speaker
Adam Kalai, Microsoft Research
Monday, November 7, 2016 - 12:30pm
Computer Science 105

Meta-unsupervised-learning: a principled approach to unsupervised learning

Unsupervised Learning and exploratory data analysis are among the most
important and yet murkiest areas within machine learning. Debates rage even
about how to choose which objective function to optimize. We introduce a
principled data-driven approach: “meta-unsupervised-learning” using a
collection of related or unrelated learning problems. We present simple
agnostic models and algorithms illustrating how the meta approach
circumvents impossibility results for novel "meta" problems such as
meta-clustering, meta-outlier-removal, meta-feature-selection, and
meta-embedding. We also present empirical results showing how the meta
approach improves over standard techniques for problems such as outlier
removal and choosing a clustering algorithm and a number of clusters. We
also train an unsupervised neural network that learns from prior supervised
classification problems drawn from learning problems at openml.org.

Joint work with Vikas Garg from MIT

Adam Tauman Kalai received his BA from Harvard, and MA and PhD under the
supervision of Avrim Blum from CMU. After an NSF postdoctoral fellowship at
M.I.T. with Santosh Vempala, he served as an assistant professor at the
Toyota Technological institute at Chicago and then at Georgia Tech. He is
now a Principal Researcher at Microsoft Research New England. His honors
include an NSF CAREER award and an Alfred P. Sloan fellowship. His research
focuses on machine learning, human computation, and algorithms.

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