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CATEGORIES:AI+Pizza
SUMMARY:AI+Pizza February 2018 - Microsoft Research/Univer
sity of Cambridge
DTSTART;TZID=Europe/London:20180216T173000
DTEND;TZID=Europe/London:20180216T190000
UID:TALK101422AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/101422
DESCRIPTION:Speaker 1: Konstantina Palla (MSR Cambridge). \nTi
tle: Bayesian nonparametrics for Sparse Dynamic Ne
tworks. Abstract: We propose a Bayesian nonparamet
ric prior for time-varying networks. To each node
of the network is associated a positive parameter\
, modelling the sociability of that node. Sociabil
ities are assumed to evolve over time and are mode
lled via a dynamic point process model. The model
is able to (a) capture smooth evolution of the int
eraction between nodes\, allowing edges to appear/
disappear over time (b) capture long term evolutio
n of the sociabilities (c) and yield sparse graphs
\, where the number of edges grows subquadraticall
y with the number of nodes. The evolution of the s
ociabilities is described by a tractable time-vary
ing gamma process. We provide some theoretical ins
ights into the model and apply it to real world da
tasets.\n\nSpeaker 2: Mark Rowland (Cambridge univ
ersity). \nTitle: Analysing Distributional Reinfor
cement Learning. Abstract: Distributional approach
es to value-based reinforcement learning use the e
ntire distribution of returns\, rather than just t
heir expected values. Recently\, these methods hav
e been shown to yield state-of-the-art performance
on a variety of RL tasks. In this talk\, I'll rec
ap some of the main algorithms and results in dist
ributional reinforcement learning\, and give an ov
erview of some recent theoretical developments (jo
int work with Marc G. Bellemare\, Will Dabney\, RĂ©
mi Munos\, and Yee Whye Teh).\n\n\n
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station R
oad\, Cambridge\, CB1 2FB
CONTACT:Microsoft Research Cambridge Talks Admins
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