|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Using Context and Insight for the Analysis of LittleData?
If you have a question about this talk, please contact Zoubin Ghahramani.
We live in the era of Big Data with many interesting challenges for Machine Learning. In the opposite regime of “Little Data”, a limited population size, limited measurement time or a limited budget constrains the amount of data available. Examples include magnetic resonance imaging, microarray experiments or portfolio management. To obtain sensible predictions it is then crucial to take modeling assumptions into account. In this talk I will cover challenges, both computational and conceptual, that come up in the presence of a limited amount of data and how they can be overcome. I will focus on causality and high-dimensional covariance estimation and present a new algorithm that allows to compute an asymptotically unbiased, sparse and positive semidefinite estimator for covariance matrices using the SCAD penalty from high-dimensional statistics.
This talk is part of the Machine Learning @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsBiological Anthropology Easter Term Seminars 2012 Optimization and Incentives Seminar Wall Street meets Lincoln's Inn!
Other talksAn introduction to the Mondrian Process Valuing the economic benefits of complex interventions: when maximising health is not sufficient Marvellous Mammillarias The 2015 Smoking Science Summit Assessing causality in perinatal and developmental epidemiology Title 'TBA'