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DTSTAMP:20260510T013157Z
DESCRIPTION: \n\n\n	\n		\n			\n				Distributed kernel regression for large-scale data
 \n			\n		\n	\n\n\n \n\nAbstract:\n\nIn modern scientific research\, massive data
 sets with huge numbers of observations are frequently encountered. To faci
 litate the computational process\, a divide-and-conquer scheme is often us
 ed for the analysis of big data. In such a strategy\, a full dataset is fi
 rst split into several manageable segments\; the final output is then aggr
 egated from the individual outputs of the segments. Despite its popularity
  in practice\, it remains largely unknown that whether such a distributive
  strategy provides valid theoretical inferences to the original data\; if 
 so\, how efficient does it work? In this talk\, I address these fundamenta
 l issues for the non-parametric distributed kernel regression\, where accu
 rate prediction is the main learning task. I will begin with the naive sim
 ple averaging algorithm and then talk about an improved approach via ADMM.
  The promising preference of these methods is supported by both simulation
  and real data examples.\n\nSpeaker: Chen Xu is an Assistant Professor in 
 the Department of Mathematics and Statistics\, University of Ottawa.\n\n 
 \n
DTSTART:20170331T193000Z
DTEND:20170331T193000Z
LOCATION:BURN 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Chen Xu\, University of Ottawa
URL:https://www.mcgill.ca/mathstat/channels/event/chen-xu-university-ottawa
 -267313
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