BEGIN:VCALENDAR
PRODID:-//SiteBuilder 2//University of ÌÇÐÄTV ITS Web Team//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-TIMEZONE:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:VTIMEZONE
TZID:Europe/London
LAST-MODIFIED:20201010T011803Z
TZURL:http://tzurl.org/zoneinfo/Europe/London
X-LIC-LOCATION:Europe/London
X-PROLEPTIC-TZNAME:LMT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+000115
TZOFFSETTO:+0000
DTSTART:18471201T000000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19160521T020000
RDATE:19170408T020000
RDATE:19180324T020000
RDATE:19190330T020000
RDATE:19200328T020000
RDATE:19210403T020000
RDATE:19220326T020000
RDATE:19230422T020000
RDATE:19240413T020000
RDATE:19270410T020000
RDATE:19300413T020000
RDATE:19330409T020000
RDATE:19340422T020000
RDATE:19350414T020000
RDATE:19380410T020000
RDATE:19390416T020000
RDATE:19400225T020000
RDATE:19460414T020000
RDATE:19470316T020000
RDATE:19480314T020000
RDATE:19490403T020000
RDATE:19530419T020000
RDATE:19540411T020000
RDATE:19570414T020000
RDATE:19600410T020000
RDATE:19680218T020000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19161001T030000
RDATE:19170917T030000
RDATE:19180930T030000
RDATE:19190929T030000
RDATE:19201025T030000
RDATE:19211003T030000
RDATE:19221008T030000
RDATE:19391119T030000
RDATE:19471102T030000
RDATE:19481031T030000
RDATE:19491030T030000
RDATE:19711031T030000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19230916T030000
RRULE:FREQ=YEARLY;UNTIL=19240921T020000Z;BYMONTH=9;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19250419T020000
RRULE:FREQ=YEARLY;UNTIL=19260418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19251004T030000
RRULE:FREQ=YEARLY;UNTIL=19381002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19280422T020000
RRULE:FREQ=YEARLY;UNTIL=19290421T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19310419T020000
RRULE:FREQ=YEARLY;UNTIL=19320417T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19360419T020000
RRULE:FREQ=YEARLY;UNTIL=19370418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19410504T020000
RDATE:19450402T020000
RDATE:19470413T020000
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19410810T030000
RRULE:FREQ=YEARLY;UNTIL=19430815T010000Z;BYMONTH=8;BYMONTHDAY=9,10,11,12,
 13,14,15;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19420405T020000
RRULE:FREQ=YEARLY;UNTIL=19440402T010000Z;BYMONTH=4;BYMONTHDAY=2,3,4,5,6,7
 ,8;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19440917T030000
RDATE:19450715T030000
RDATE:19470810T030000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19451007T030000
RRULE:FREQ=YEARLY;UNTIL=19461006T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19500416T020000
RRULE:FREQ=YEARLY;UNTIL=19520420T020000Z;BYMONTH=4;BYMONTHDAY=14,15,16,17
 ,18,19,20;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19501022T030000
RRULE:FREQ=YEARLY;UNTIL=19521026T020000Z;BYMONTH=10;BYMONTHDAY=21,22,23,2
 4,25,26,27;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19531004T030000
RRULE:FREQ=YEARLY;UNTIL=19601002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19550417T020000
RRULE:FREQ=YEARLY;UNTIL=19560422T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19580420T020000
RRULE:FREQ=YEARLY;UNTIL=19590419T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19610326T020000
RRULE:FREQ=YEARLY;UNTIL=19630331T020000Z;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19611029T030000
RRULE:FREQ=YEARLY;UNTIL=19671029T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19640322T020000
RRULE:FREQ=YEARLY;UNTIL=19670319T020000Z;BYMONTH=3;BYMONTHDAY=19,20,21,22
 ,23,24,25;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:BST
TZOFFSETFROM:+0100
TZOFFSETTO:+0100
DTSTART:19681026T230000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19720319T020000
RRULE:FREQ=YEARLY;UNTIL=19800316T020000Z;BYMONTH=3;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19721029T030000
RRULE:FREQ=YEARLY;UNTIL=19801026T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19810329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19811025T020000
RRULE:FREQ=YEARLY;UNTIL=19891029T010000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19901028T020000
RRULE:FREQ=YEARLY;UNTIL=19951022T010000Z;BYMONTH=10;BYDAY=4SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
DTSTART:19960101T000000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19961027T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260501T123906Z
DTSTART;VALUE=DATE-TIME:20241015T140000
DTEND;VALUE=DATE-TIME:20241015T150000
SUMMARY:CS Colloquium + Foundations of AI Seminar: Arthur Gretton (UCL)
TZID:Europe/London
UID:20241015-8a17841b9198c8bf01919da0cc7d0636@warwick.ac.uk
CREATED:20240829T101815Z
DESCRIPTION:Title: Causal Effect Estimation with Context and Confounders 
 Abstract: A fundamental causal modelling task is to predict the effect o
 f an intervention (or treatment) on an outcome\, given context/covariate
 s. Examples include predicting the effect of a medical treatment on pati
 ent health given patient symptoms and demographic information\, or predi
 cting the effect of ticket pricing on airline sales given seasonal fluct
 uations in demand. The problem becomes especially challenging when the t
 reatment and context are complex (for instance\, "treatment" might be a 
 web ad design or a radiotherapy plan)\, and when only observational data
  is available (i.e.\, we have access to historical data\, but cannot int
 ervene ourselves). The challenge is greater still when the covariates ar
 e not observed\, and constitute a hidden source of confounding. I will g
 ive an overview of some practical tools and methods for estimating causa
 l effects of complex\, high dimensional treatments from observational da
 ta. The approach is based on conditional feature means\, which represent
  conditional expectations of relevant model features. These features can
  be deep neural nets (adaptive\, finite dimensional\, learned from data)
 \, or kernel features (fixed\, infinite dimensional\, enforcing smoothne
 ss). When hidden confounding is present\, a neural net implementation of
  instrumental variable regression can be used to correct for this confou
 nding. The methods will be applied to modelling employment outcomes for 
 the US Job Corps program for Disadvantaged Youth\, and in policy evaluat
 ion for reinforcement learning. Bio: Arthur Gretton is a Professor with 
 the Gatsby Computational Neuroscience Unit\, and director of the Centre 
 for Computational Statistics and Machine Learning (CSML) at UCL\; and a 
 research scientist at Google Deepmind. His recent research interests inc
 lude causal inference and representation learning\, design and training 
 of generative models\, and nonparametric hypothesis testing. Arthur has 
 been an associate editor at IEEE Transactions on Pattern Analysis and Ma
 chine Intelligence\, an Action Editor for JMLR\, a Senior Area Chair for
  NeurIPS (2018\,2021) and ICML (2022)\, a member of the COLT Program Com
 mittee in 2013\, and a member of Royal Statistical Society Research Sect
 ion Committee since January 2020. Arthur was program co-chair for AISTAT
 S in 2016\, tutorials co-chair for ICML 2018\, workshops co-chair for IC
 ML 2019\, program co-chair for the Dali workshop in 2019\, and co-organs
 ier of the Machine Learning Summer School 2019 in London. See also https
 ://faiseminarswarwick.github.io/speakers/a-gretton.html
LOCATION:
CATEGORIES:FAIS Seminar,Computer Science Colloquium
LAST-MODIFIED:20240829T101815Z
ORGANIZER;CN=Markus Brill:
END:VEVENT
END:VCALENDAR