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SUMMARY:New Theory and Methods for Mondrian Random Forests
DTSTART:20231207T161500
DTEND:20231207T171500
DTSTAMP:20260408T121717Z
UID:c09c0150bc83c66f9bcaa8ff5caeec96c7372f5e834a2da546a413b2
CATEGORIES:Conferences - Seminars
DESCRIPTION:William Underwood – Princeton University\nPresentation in M
 athematics\n\nRandom forests are popular methods for classification and re
 gression\, and many different variants have been proposed. One interesting
  example is the Mondrian random forest\, in which the underlying trees are
  constructed according to a Mondrian process. In this talk we give a centr
 al limit theorem for the estimates made by a Mondrian random forest in the
  regression setting. When combined with a bias characterization and a cons
 istent variance estimator\, this allows one to perform asymptotically vali
 d statistical inference\, such as constructing confidence intervals\, on t
 he unknown regression function. We also provide a debiasing procedure for 
 Mondrian random forests which allows them to achieve minimax-optimal estim
 ation rates.\n\n 
LOCATION:https://epfl.zoom.us/j/61704938181
STATUS:CONFIRMED
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