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UID:www.tcs.tifr.res.in/event/1249
DTSTAMP:20230914T125956Z
SUMMARY:Best arm identification in the fixed confidence setting
DESCRIPTION:Speaker: Anirban Bhattacharjee\n\nAbstract: \nMulti-armed bandi
ts are sequential decision-making problems represented as a collection of
probability distributions that one can sample from at every time step. One
way to approach bandit problems is with the target of minimizing the expe
cted regret (penalty for not sampling from the distribution with the highe
st mean)\, given a fixed number of times that one can draw samples from th
is distribution (called the sampling budget). The second way of approachin
g these problems is with the target of minimizing the expected number of t
imes these distributions need to be sampled from (called the sampling comp
lexity)\, in order to declare the "best arm" with reasonable certainty\, g
iven the extent of certainty that is desired. We shall look at the "best a
rm" approach to multi-armed bandits when all the probability distributions
belong to a single parameter exponential family\, the lower bound on the
sampling complexity\, and how this lower bound may be asymptotically met.\
n
URL:https://www.tcs.tifr.res.in/web/events/1249
DTSTART;TZID=Asia/Kolkata:20221104T160000
DTEND;TZID=Asia/Kolkata:20221104T170000
LOCATION:A201
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