BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20191103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:69d8c3691a7b9
DTSTART;TZID=America/Toronto:20200130T100000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200130T100000
URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/department
 -seminar-hyukjun-jay-gweon-western-university
LOCATION:M3 - Mathematics 3 200 University Avenue West Room 3127 Waterloo O
 N N2L 3G1 Canada
SUMMARY:Department seminar by Hyukjun (Jay) Gweon\, Western University
CLASS:PUBLIC
DESCRIPTION:BATCH-MODE ACTIVE LEARNING FOR REGRESSION AND ITS APPLICATION T
 O THE\nVALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS\n\nSupervised learni
 ng algorithms require a sufficient amount of labeled\ndata to construct an
  accurate predictive model. In practice\,\ncollecting labeled data may be 
 extremely time-consuming while\nunlabeled data can be easily accessed. In 
 a situation where labeled\ndata are insufficient for a prediction model to
  perform well and the\nbudget for an additional data collection is limited
 \, it is important\nto effectively select objects to be labeled based on w
 hether they\ncontribute to a great improvement in the model's performance.
  In this\ntalk\, I will focus on the idea of active learning that aims to 
 train\nan accurate prediction model with minimum labeling cost. In\npartic
 ular\, I will present batch-mode active learning for regression\nproblems.
  Based on random forest\, I will propose two effective random\nsampling al
 gorithms that consider the prediction ambiguities and\ndiversities of unla
 beled objects as measures of their informativeness.\nEmpirical results on 
 an insurance data set demonstrate the\neffectiveness of the proposed appro
 aches in valuing large variable\nannuity portfolios (which is a practical 
 problem in the actuarial\nfield). Additionally\, comparisons with the exis
 ting framework that\nrelies on a sequential combination of unsupervised an
 d supervised\nlearning algorithms are also investigated.
DTSTAMP:20260410T093121Z
END:VEVENT
END:VCALENDAR