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BEGIN:VEVENT
DTSTAMP:20260427T211107Z
DTSTART;VALUE=DATE-TIME:20230606T170000
DTEND;VALUE=DATE-TIME:20230606T180000
SUMMARY:YRM Week 7: David Huk & Alexander Kent
TZID:Europe/London
UID:20230606-8a17841b884dd332018890a4b07d5520@warwick.ac.uk
CREATED:20230606T121812Z
DESCRIPTION:David Huk Title: Probabilistic forecasting with censored spat
 ial copulas via scoring rules Abstract: This work develops a novel metho
 d for generating conditional probabilistic rainfall forecasts with tempo
 ral and spatial dependence. A two-step procedure is employed. Firstly\, 
 marginal location-specific distributions are modelled independently of o
 ne another. Secondly\, a spatial dependency structure is learned in orde
 r to make these marginal distributions spatially coherent. To learn marg
 inal distributions over rainfall values\, we propose a class of models t
 ermed Joint Generalised Neural Models (JGNMs) which expand the linear pa
 rt of generalised linear models with a deep neural network allowing them
  to take into account non-linear trends of the data while learning the p
 arameters for a distribution over the outcome space. In order to underst
 and the spatial dependency structure of the data\, a model based on cens
 ored copulas is presented and trained via scoring rules. Utilising the u
 nderlying spatial structure as a starting point\, we construct a matrix 
 of pair-wise distances between locations which is then transformed by a 
 Gaussian Process Kernel depending on a few parameters. To estimate these
  parameters\, we propose a general framework for the estimation of Gauss
 ian copulas relying on scoring rules as a measure of divergence between 
 distributions. Uniting our two contributions\, namely the JGNM and the C
 ensored Spatial Copulas into a single model\, we get a probabilistic mod
 el capable of generating possible scenarios on short to long-term timesc
 ales\, able to be evaluated at any given location\, seen or unseen. We s
 how an application of it to a precipitation downscaling problem on a lar
 ge UK rainfall dataset and compare it to existing methods. Alexander Ken
 t Title: Local Differential Privacy with Time Series Data Abstract: The 
 issue of maintaining user privacy whilst simultaneously preserving stati
 stical utility of user data has become increasingly prevalent in recent 
 decades. Government regulation and user demands require data to be kept 
 secure and used only for reasonable purposes\, whilst modern machine lea
 rning methods allow for remarkable results provided large amounts of dat
 a can be obtained and used freely. As a result\, privatising data so tha
 t it can be used for statistical purposes whilst satisfying these constr
 aints is of importance\, and Local Differential Privacy has arisen as th
 e leading framework for carrying out statistical procedures on data whil
 st maintaining user privacy in the case where there is no trusted aggreg
 ator for the data. Whilst there has been a large amount of research in L
 ocal Differential Privacy recently\, developments regarding time series 
 data are less common despite the prevalence and importance of this kind 
 of data\, primarily due to the difficulties that arise as a result of pr
 ivatising such data. In this talk\, I will motivate and introduce the id
 eas of differential privacy and summarise the result of my work with Dr 
 Tom Berrett and Dr Yi Yu so far. I will also briefly introduce minimax t
 heory as a framework for evaluating the performance of any (not necessar
 ily private) estimator\, and present the surprising minimax performance 
 that arises due to this intersection of Local Differential Privacy and t
 ime series data.
LOCATION:
URL:
ATTACH:
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LAST-MODIFIED:20230606T121812Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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