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BEGIN:VEVENT
DTSTAMP:20241022T103315Z
DTSTART:20241113T110000Z
DTEND:20241113T120000Z
SUMMARY:AI-Fun with ELLIS Seminar | Jack Liell-Cock: Compositional imprec
 ise probability
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}hkq-m0dt6o9
 u-17y6xj
DESCRIPTION:The Manchester Centre for AI Fundamentals and Manchester's EL
 LIS Unit are co-hosting a series of seminars featuring expert researcher
 s working in the fundamentals of AI. \n\nJack Liell-Cock is a PhD studen
 t at Keble College\, Oxford\, under the supervision of Jeremy Gibbons an
 d Sam Staton. \n\nTitle: Compositional imprecise probability\n\nAbstract
 : \nImprecise probability concerns uncertainty about which probability d
 istributions to use. It has applications in robust statistics and AI saf
 ety. Imprecise probability can be modelled in various ways\, such as by 
 convex sets of probability distributions.\n\nWe look at programming lang
 uage models for imprecise probability. Our desiderata are that we would 
 like our model to support all kinds of composition\, categorical and mon
 oidal\; in other words\, guided by dataflow diagrams. Another equivalent
  perspective is that we would like a model of synthetic probability in t
 he sense of Markov categories.\n\nThe leading monad-based approach to im
 precise probability is not fully compositional because the monad involve
 d is not commutative\, meaning it does not have a proper monoidal struct
 ure. In this work\, we provide a new fully compositional account. The ke
 y idea is to name the non-deterministic choices. To manage the renamings
  and disjointness of names\, we use graded monads. We show that the resu
 lting compositional model is maximal and relate it with the earlier mona
 d approach\, proving that we obtain tighter bounds on the uncertainty.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Lecture Theatre 1.4\, Kilburn Building\, Manchester
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