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f 2 2 F F to o the best t possible measurable function, , but t it has nothingto dowith h the
geometricstructureofF. Themarginisdeterminedforeveryfseparately,because
f
doesnotdependonthechoiceofF atall.
Inthesecond,\learningtheory"setup,wedonotassumethatthetargetfunction
f
belongs to F. . The e aimis s to o construct a a function
b
f whose e risk k is s as close as
possibletothatofthebestelementf
F
2F. And,assumingthattheexcesslossclass
L
F
satises theBernsteincondition(BC for short) one canimprove the errorrate
(see,e.g.,[20,5]).
Atarstglance,MAandBC(for=1)shareverystrongsimilarities. Indeed,
sayingthatL
F
isa(1;B)-Bernsteinclassmeansthatforeveryf2F
E
(Q(Z;f) Q(Z;f
F
))
2
B(R(f) R(f
F
));
butnevertheless,theyaredierent.Indeed,aswementioned,MAisonlyamatterof
concentration(andclassicalstatisticsquestionsaremostlyaquestionofthetradeo
between concentrationandcomplexity). . On n theother r hand, , BC involves alot t of
geometryofthefunctionclassF,becausef
F
mightchangesignicantlybyaddinga
singlefunctiontoF orbyremovingone. Infact,thedicultyof\learningtheory"
problemsis determinedby thetradeo betweenconcentrationandcomplexity, , and
thegeometryofthegivenclass,sinceonemeasurestheperformanceofthelearning
algorithmrelativetothebestintheclass. Assumingthatf
2F,asisusuallydone
inclassicalstatistics,exemptsonefromtheneedtoconsiderthegeometryofF,but
wedonothavethatfreedomintheaggregationframework.Indeed,sinceintheAEW
algorithmtheestimatorisdeterminedbytheempiricalmeansR
n
(f) R
n
(f
F
),itis
alearningproblemratherthanaprobleminclassicalstatistics(despitethefactthat
ithasbeenusedinstatisticalframeworkstoconstructadaptiveestimators,see,for
example,[4,11,14,23,6,18,25,2,28]).Therefore,becauseofitsnature,aggregation
proceduresliketheAEWaremorenaturalunderaBCassumptionandnottheMA
one(aby-product of TheoremAis thatthe MAcannot improve the performance
of AEWsince inTheoremA’ssetupMAis satisedwiththebestpossiblemargin
parameter=1).
3 Preliminaryresultsongaussianapproximation
Our starting point is s the e Berry-Esseen Theorem on gaussian approximation. . Let
(W
n
)
n2N
beasequenceofi.i.d.,meanzerorandomvariableswithvariance1,setgto
beastandardGaussianvariableandput
X
n
=
1
p
n
Xn
i=1
W
i
:
11
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Theorem3.1 ([21])There exists s an absolute constant A > > 0 0 such that for r every
integern,
sup
x2R
P[
X
n
x] P[gx]
AEjW
1
j
3
p
n
:
FromhereonwewilldenotebyAtheconstantappearinginTheorem3.1.
Whenthetailbehavior oftheW
i
hasasub-exponentialdecay,thegaussianap-
proximationcanbeimproved. Indeed,recallthatareal-valuedrandomvariableW
belongstoL
forsome1ifthereexists0<c<1suchthat
Eexp
jWj
=c
2:
(3.1)
Theinmumoverallconstantscforwhich(3.1)holdsdenesanOrlicznorm,which
iscalledthe 
normandisdenotedbykk
.FormorefactsonOrlicznormssee,
forinstance,[26]and[22].
Proposition3.2(Chapter5in[21]) ForeveryL>0thereexistconstantsB
0
;c
1
andc
2
thatdependonlyonLforwhichthefollowingholds. IfkWk
1
Lthenfor
anyx0suchthatxB
0
n
1=6
,
P[
X
n
x]=P[gx]exp
x
3
EW
3
6
p
n

1+O
x+1
p
n

and
P[
X
n
 x]=P[g x]exp
x
3
EW
3
6
p
n

1+O
x+1
p
n

;
wherebyv=O(u)wemeanthat c
1
uvc
1
u.
Inparticular,ifjxjB
0
n
1=6
andEW
3
=0then
jP[
X
n
x] P[gx]jc
2
(n
1=2
exp( x
2
=2)):
FromhereonwewilldenotebyB
0
theconstantappearinginProposition3.2.
4 ProofofTheoremA
Before presenting the proof ofTheoremA,letus introduce the followingnotation.
Givenaprobabilitymeasureand(Z
i
)
n
i=1
selectedindependentlyaccordingto,we
setP
n
=n
1
P
n
i=1
Z
i
theempiricalmeasuresupportedon(Z
i
)
n
i=1
. WedenotebyP
theexpectationE
.Fromhereon,wewillassumethatT1andrecallthatnisan
oddinteger.
LetY =0anddeneXbyP[X=1]=1=2 n
1=2
andP[X= 1]=1=2+n
1=2
.
Letf
1
=1I
[0;1]
andf
2
=1I
[ 1;0]
,andconsiderthedictionary F=ff
1
;f
2
g. Itiseasy
12
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toverifythatthebestfunctioninF (theoracle)withrespecttothequadraticrisk
isf
1
andthattheexcesslossfunctionoff
2
,L
2
=f
2
2
f
2
1
=f
2
f
1
,satisesthat
L
2
(X)= X; ; EL
2
(X)=2n
1=2
and
2
=E
L
2
(X) EL
2
(X)
2
=1 4=n:
Toshortennotation,wedenePL
2
=EL
2
(X)andP
n
L
2
=n
1
P
n
i=1
L
2
(X
i
).
An important parameter r which h is at the e heart of this s counter-example is the
Bernsteinconstant(whichisverybadinthiscase):
=
E(f
1
f
2
)
2
PL
2
=
p
n
2
(4.1)
AstraightforwardcomputationshowsthatAEWonF withtemperatureT T isgiven
by
~
f
AEW
=
b
1
f
1
+(1 
b
1
)f
2
;
b
1
=
1
1+exp
n
T
P
n
L
2
;
andthat,forh()=+(1 )denedforall2[0;1],wehave
E[R(
~
f
AEW
) R(f
1
)]=E
h
b
1
b
1
(1 
b
1
)
i
PL
2
=E
1 h(
b
1
)
PL
2
=E
h
Z
1
0
h
0
(t)P[
b
1
t]dt
i
PL
2
=
1+
Z
1
0
(2t (1+))P[
b
1
t]dt
PL
2
=
1+
Z
1
0
(2t (1+))P[P
n
L
2
 (t)]dt
PL
2
;
(4.2)
where (t)isanincreasingfunctiondenedforanyt2(0;1)by
(t)=
T
n
log
t
1 t
:
Inparticular,
E
h
R(
~
f
AEW
) R(f
1
)
i
=[I
1
+I
2
]PL
2
;
for
I
1
=
Z
1
0
(2t (1+))P[P
n
L
2
 (t)]dt+1
and
I
2
=
Z
1
 1
(2t (1+))P[P
n
L
2
 (t)]dt:
First,letus boundI
1
frombelow. Tothatendoneshouldnoticethefollowing
facts.First,thatforevery0t
1
,1+ 2t0and
Z
1
0
(2t (1+))dt= 1:
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Second,ifwesetE=exp(nPL
2
=T),thenforT .
p
n=logn,0<(1+E)
1

1
.In
particular,thisholdsunderourassumptionthatT1.Also,because isincreasing
thenfor(1+E)
1
t
1
, (t)
(1+E)
1
= PL
2
.Therefore,
I
1
=
Z
1
0
(2t (1+))P[P
n
L
2
 (t)]dt+1
=
Z
1
0
(2t (1+))(P[P
n
L
2
 (t)] 1)dt
Z
1
(1+E) 1
(1+ 2t)P[P
n
L
2
< (t)]dt
Z
 1
(1+E) 1
(1+ 2t)dtP
(
p
n=)(P
n
L
2
PL
2
)<(
p
n=)( 2PL
2
)
Z
1
(1+E) 1
(1+ 2t)dt
P[g 8] A=
p
n
c
0
>0;
whereinthe laststepwe usedtheBerry-EsseenTheorem,that jL
2
j 1andthat
n8_(2A=P[g 8])
2
,implyingthat0<c
0
<1=2.
LetusturntoalowerboundforI
2
.Applyingachangeofvariablest7!1+
1
u
inthesecondtermofI
2
,itisevidentthat
I
2
=
Z
+1
2
 1
(2t (1+))P[P
n
L
2
 (t)]dt+
Z
1
+1
2
(2t (1+))P[P
n
L
2
 (t)]dt
=
Z
+1
2
 1
(2t (1+))P
(t)P
n
L
2
<
1+
1
t

dt=I
3
+I
4
for
I
3
=
Z
(1+c
0
=4) 1
 1
(2t (1+))P
(t)P
n
L
2
<
1+
1
t

dt
and
I
4
=
Z
+1
2
(1+c
0
=4)
1
(2t (1+))P
(t)P
n
L
2
<
1+
1
t

dt:
ToestimateI
3
,notethat2t (1+)0fort2[
1
;(+1)=(2)]andthus
I
3
Z
(1+c
0
=4)
1
 1
(2t (1+))dt
c
0
4
1+
1
 
c
0
3
;
14
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forourchoiceof.
ThenalstepoftheproofistoboundI
4
,andinparticulartoshowthatforsmall
valuesofT,I
4
 c
0
=3.
Forany0<t(+1)=(2),considertheintervalsI
T
(t)=
n (t);n (1+
1
t)
,
and set N
T
(t) = = jfI
T
(t)\Zgj, which h is the e number of integers in I
T
(t). Since
L
2
(X)= Xthen
P
(t)P
n
L
2
<
1+
1
t

=P
"
Xn
i=1
X
i
2I
T
(t)
#
=P
T
(t):
Recall that t X X 2 f 1;1g g and thus s P[
P
i
X
i
2I
T
(t)] = = P[
P
i
X
i
2I
T
(t)\Z].
Sincen (t)isincreasingandnonnegativefort>1=2thenif1=2<t(+1)=(2)
it follows that0 <n (t)< n (1+1= t) ) <1, , providedthatT T 1. . Thus,for
suchvaluesoft,N
T
(t)=0,implyingthatP
T
(t)=0.Ontheotherhand,ift1=2,
thenf0gI
T
(t)\Z. Inparticular,ifN
T
(t)=1thenI
T
(t)\Z=f0gandsincen
isoddthenP
T
(t)=P[
P
n
i=1
X
i
=0]=0. Otherwise,N
T
(t)2whichimpliesthat
N
T
(t)2
T
(t)where
T
(t)isthelengthofI
T
(t),givenby
T
(t)=n( (1+
1
t)  (t))=Tlog
(1 t)(+1 t)
t(t 1)
:
Therefore,foreverytinourrange,
P
T
(t)N
T
(t) max
k2I
T
(t)
P
"
Xn
i=1
X
i
=k
#
2
T
(t)max
k2Z
P
"
Xn
i=1
X
i
=k
#
:
Since2t (1+)0forevery0<t(+1)=(2)itisevidentthat
I
4
2Tmax
k2Z
P
"
n
X
i=1
X
i
=k
#
Z
+1
2
(1+c
0
=4) 1
(2t (1+))log
(1 t)(+1 t)
t(t 1)
dt:
Onemayshowthatmax
k2Z
P[
P
n
i=1
X
i
=k]isoftheorderofn
1=2
eitherbyadirect
computationor by the Berry-EsseenTheorem. . Moreover,forany y (1+c
0
=4)
1
t(+1)=(2),onehast 1c
0
(4+c
0
)
1
t,andthus,
log
(1 t)(+1 t)
t(t 1)
log
2(4+c
0
)
c
0
t2
:
Therefore, combining the two observations s with h a change of variables u = Ctfor
C = = (c
0
=(2(4+c
0
)))
1=2
, it t is evident that there e are e absolute constants s c
1
;c
2
for
which
I
4
c
1
T
p
n
Z
C(+1)
2
C(1+c
0
=4) 1
1+ 2u=C
(logu)du c
2
T
p
n
:
15
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Hence,thereisanabsoluteconstantc
3
suchthatifTc
3
thenI
4
 c
0
=3,implying
that
E
h
R(
~
f
AEW
) R(f
1
)
i
c
0
3
p
n
andprovingtherstpartofTheoremA.
Toprovethesecondpartoftheclaim,notethatbytheBerry-EsseenTheorem,
foreveryx2R,withprobabilitygreaterthanP[gx] 2A=
p
n
p
n
(L
2
)
(P
n
L
2
PL
2
)x:
Thus,ifn islargeenoughtoensurethatP[g 4] 2A=
p
nP[g 4]=2=c
4
andtakingx= 4,thenwithprobabilityatleastc
4
,P
n
L
2
 n
1=2
.Onthatevent
b
1
exp( 
p
n=T),whichyieldsthat
R(
~
f
AEW
) R(f
1
)=
b
1
b
1
(1 
b
1
)
PL
2
PL
2
=4=n
1=2
=2;
providedthatT.
p
n=logn.
5 ProofofTheoremB
Therst stepintheproofofTheoremBis ageneralstatementaboutamonotone
rearrangementofindependentrandomvariablesthatareclosetobeinggaussian.
LetW beameanzero,varianceonerandomvariable,thatisabsolutelycontinuous
withrespecttotheLebesguemeasure. Assumefurtherthat t jWjhas anitethird
moment(infact,therandomvariableswewillbeinterestedinwillbebounded)and
set(W)=AEjWj
3
,whereAistheconstantappearingintheBerry-EsseenTheorem
(Theorem3.1). Let t W
1
;:::;W
n
beindependentrandomvariablesdistributedas W
andset
X =n
1=2
P
n
i=1
W
i
. Let t (
X
j
)
j=1
be ‘independent copies of
X,andput
1
=
1
(‘)2Rtosatisfythat
P
min
1j‘
X
j
1
(‘)
=1 
1
n
:
Note thatsuch a
1
existsbecause W W has s adensity withrespect tothe Lebesgue
measure.
ThroughouttheproofofTheoremBwewillrequirethefollowingsimpleestimates
on
1
.
Lemma5.1 Thereexistabsoluteconstantsc
0
;:::;c
3
forwhichthefollowingholds.
16
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1. If‘c
0
lognthen
1 c
1
logn
P[
X>
1
]1 
logn
:
2. If‘andnaresuchthat((W)=
p
n+(logn)=‘)<P[g< 2],then
1
 2.
3. If
1
 2andc
0
logn‘c
2
1
(W)
p
nlognthen
j
1
jlog
1=2
c
3
logn
and exp( 
2
1
=2)
logn
log
1=2
c
3
logn
:
BeforepresentingtheproofofLemma5.1,recallthatforeveryx2,
3
4
p
2
exp
x
2
=2
x
P[gx]
1
p
2
exp
x
2
=2
x
:
(5.1)
ProofofLemma5.1. Toprovetherstpart,notethatbyindependenceandsince
exp( x)1 x,
P[
X>
1
]=P[min
1j‘
X
j
>
1
]
1
=
1
n
1=‘
1 
logn
:
(5.2)
The reverse inequality follows in an identical fashion,since exp( x) )  1 x=3if
0x1.
Turningtothesecondpart,if
1
> 2then
1
n
=P[min
1j‘
X
j
 
1
]P[min
1j‘
X
j
 2]=1 (P[
X> 2])
;
implying that t P[
X    2] ]   (logn)=‘. . On n the other r hand, , by the e Berry-Esseen
Theorem, P[
X    2] ]  P[g   2] (W)=
p
n, which h is impossible e under the
assumptionsof(2).
Finally,toprove(3),oneusestheBerry-EsseenTheoremcombinedwiththelower
andupperestimatesontheGaussiantail(5.1)and(5.2). Thus,
3
4
p
2
1
j
1
j
exp
j
1
j
2
2
P[g<
1
]P[
X<
1
]+
(W)
p
n
(W)
p
n
+c
1
logn
;
and
1
p
2
1
j
1
j
exp
j
1
j
2
2
logn
(W)
p
n
:
fromwhichbothpartsofthethirdclaimfollow.
17
Proposition5.2 There existsconstantsc
1
;c
2
;c
3
andc
4
dependingonlyonkWk
2
forwhichthefollowingholds. Let2M
2
exp( c
1
n
1=3
)<1,assumethatEW
3
=0
andthat
1
=
1
(M 1) 2. . Then,
P
9j2f2;:::;Mg:
X
j
1
andforeveryk2f2;:::;Mgnfjg;
X
k
X
j

1 
1
n
c
2
1
p
n
+
(logn)
2
p
logM;
providedthatc
3
lognMc
4
p
n(logn).
Proof. Forevery2jM,let
j
=
X
j
1
and
X
k
X
j
foreveryk2f2;:::;Mgnfjg
 
:
Theevents
j
for2jMaredisjointsandthus
P
9j2f2;:::;Mg:
X
j
1
and
X
k
X
j
foreveryk2f2;:::;Mgnfjg
=P[[
M
j=2
j
]=(M 1)P[
2
]:
Sincethevariables(
X
j
)
M
j=2
areindependent,then
P[
2
]=
Z
1
1
f
X
(z)
Z
1
z+
f
X
(t)d(t)
M 2
d(z);
wheref
X
isadensityfunctionof
X withrespecttotheLebesguemeasure.
Ontheother hand,for any z 
1
,P[
X z]>0becauseof(5.2). . Hence,for
everyz
1
,
Z
1
z+
f
X
(t)d(t)=
R
z+
z
f
X
(t)d(t)
R
1
z
f
X
(t)d(t)
!
Z
1
z
f
X
(t)d(t):
(5.3)
Notethatforevery0x1,(1 x)
M 2
1 (M 2)x,andappliedto(5.3),
P[
2
]
Z
1
1
f
X
(z)
Z
1
z
f
X
(t)d(t)
M 2
d(z)
(M 2)
Z
1
1
f
X
(z)
Z
1
z
f
X
(t)d(t)
M 3
Z
z+
z
f
X
(t)d(t)
d(z)
P
X
2
1
and
X
k
X
2
; foreveryk3
T
2
=
1
M 1
P
min
2jM
X
j
1
T
2
;
18
where
T
2
=(M 2)
Z
1
1
f
X
(z)
Z
z+
z
f
X
(t)d(t)
d(z):
Recalltheif(W
i
)areindependent,meanzerorandomvariablesthenk
P
a
i
W
i
k
2
c(
P
a
2
i
kW
i
k
2
2
)
1=2
where cis anabsolute constant[26]. . Hence, , k
Xk
2
ckWk
2
,
andforanyt<0,
Z
t
1
f
X
(z)
Z
z+
z
f
X
(t)d(t)
d(z)P[
Xt]2exp( t
2
=c
2
kWk
2
2
):
Lett
0
<0besuchthat
2exp( t
2
0
=c
2
kWk
2
2
)=
p
log(M 1)
(M 1)(M 2)
:
Hence,
(M 2)
Z
t
0
1
f
X
(z)
Z
z+
z
f
X
(t)d(t)
d(z)
p
log(M 1)
M 1
:
Notethatift
0
1
thenourclaimfollows. Indeed,sinceP
min
2jM
X
j
1
1 n
1
,then
P[
0
]
1
M 1
1
n
p
log(M 1)
M 1
:
Otherwise,wesplittheinterval( 1;
1
]=( 1;t
0
)[[t
0
;
1
],andtoupperbound
T
2
itremainstocontroltheintegralonthesecondinterval[t
0
;
1
].
Recallthat W W 2 2 L
1
andthat EW
3
= 0. . Therefore, , by y Proposition 3.2, it t is
evidentthatifzandsatisfythatzz+0andjzj;jz+jB
0
n
1=6
,then
Z
z+
z
f
X
(t)d(t)=P[z
Xz+]
P[zgz+]+
B
1
p
n
exp
z
2
=2
;
(5.4)
whereB
0
andB
1
areconstantsthatdependonlyonkWk
1
.Also,foreveryz0,
P[zgz+]
1
p
2
exp
z
2
=2
Z
0
exp( zt)dt
p
2
exp
z
2
=2
: (5.5)
19
If2M
2
exp( B
2
0
n
1=3
=kWk
2
2
)< 1thenjt
0
jB
0
n
1=6
. Combining(5.4)and
(5.5)withthedenitionofT
2
,
(M 2)
Z
1
t
0
f
X
(z)
Z
z+
z
f
X
(t)d(t)
d(z)
(M 2)
B
1
p
n
+
p
2
Z
1
t
0
f
X
(z)exp
z
2
=2
d(z)
(M 2)
B
1
p
n
+
p
2
exp( 
2
1
=2)P[
X
1
]
(M 2)
B
1
p
n
+
p
2
exp( 
2
1
=2)
logn
M 1
;
wherethelastinequalityfollowsfrom(5.2).ByLemma5.1andsinceM.
p
nlogn,
(M 2)
Z
1
t
0
f
X
(z)
Z
z+
z
f
X
(t)d(t)
d(z)
c
1
p
n
+

logn
M
(logn)
p
logM
forsomeconstantc=c(),fromwhichourclaimfollows.
Next,letusdescribetheconstructionwe needfortheproofofTheoremB.Let
(X;Y)andF =ff
1
;:::;f
M
gbedenedby
Y =0;
f
1
(X)=(12)
1=4
U
1
;
f
j
(X)=(12)
1=4
(U
j
+) forevery2jM;
whereU
1
;:::;U
M
areMindependentrandomvariableswiththedensityu7 !2(u+
)1I
[ ;1 ]
(u)for0<<1=2tobexedlater. Notethatforthischoiceofdensity
function,(U
1
+)
2
isuniformlydistributedon[0;1]andthatthebestelementinF
withrespecttothequadraticriskisf
1
.
Let(U
(i)
j
:j=1;:::;M;i=1;:::;n)beafamilyofindependentrandomvariables
distributedasU
1
. Thus,for r every 1in, , f
j
(X
i
)=(12)
1=4
(U
(i)
j
+)forevery
2jM andf
1
(X
i
)=(12)
1=4
U
(i)
1
.Forevery1jMset
R
j
=
r
12
n
Xn
i=1
(U
(i)
j
+)
2
E(U
(i)
j
+)
2
!
;
andobservethatifW =
p
12
(U+)
2
E(U+)
2
thenWisameanzero,variance
1randomvariablethatisabsolutelycontinuouswithrespecttotheLebesguemeasure;
20
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