In elimination style algorithms data is not reused

round G round This is b c of difficulties w deputerce

Input X

for f 1,2Player Choong x ex Xe closer using cxs.gs

Nature reveals yet 209kt tEe vN 0,1A iid

consider 15

Fe xsxstYfE xsy Ys xstO.ee

o Fixx X.ITxsstlP

cz E o.sse EEkIELexp hLz.E o.s

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Cannot the IEC 3 inside product b c It depends

on SES sct

Xsisindepe.de ofEs

Lea Peta formalized cleaned up literature

on self normalized bonds

Fix Recall from last time E Exacs'T sys

Z G O Fx z.LI xsxstlcE 09

E kztlcz.es i iHO O.llizxsxsy9

This process Td JIMI Highrmb

self normalized because coff 0 12 Essex

Lemming Fix 8 0 Let ye LXt 07 Et

where Et N 0,1 It chosen based on

Ius Ys If GEUM Se where

Ve D Xsxst VI

St XsYs

then

PH t cN oe o.lt VrH04htEHogTNtffIT es

Note to lNt E dlogfdfj.TLif 113411 L for all t

Now we have confidence bound for

arbitrary sequence of Meas Opens

door to new algorithms

Recall UCB from MAB MAB x eitoria

Idea For each XEX algorithm

construct confidence bond arund each X I

a d then pull arm w highest uppercut

bond Intuit If UCB is very high Her

that arm either has very large reward

OR has not been sample enough finesand sampling shrinks co 6 bond

Input Xfor 1,2

construct confidence set Ct OaECE W.p I S htt

UCB FEE LO K

Play Xea UCB x

Nature reveal Yei LKe 07 TEE

Idea Let G 40 110 0711 1E Bt

whee 13 rrllo.lt osEogTNtffIT

Let fld to III xx't VIIGCN EEE Hall a HIT

a d t.eu YoxffH There

fill fld

gal gun

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Regret of UCB X ears Logo

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