Table Of ContentBounding errors of Expectation-Propagation
6 GuillaumeDehaene SimonBarthelmé
1 UniversityofGeneva CNRS,Gipsa-lab
0 [email protected] [email protected]
2
n
a
Abstract
J
1
1
ExpectationPropagationisaverypopularalgorithmforvariationalinference,but
] comeswithfewtheoreticalguarantees. Inthisarticle, we provethattheapprox-
O
imationerrorsmadebyEPcan bebounded. Ourboundshavean asymptoticin-
C terpretationinthenumbernofdatapoints,whichallowsustostudyEP’sconver-
. gencewithrespecttothetrueposterior. Inparticular,weshowthatEPconverges
t
a atarateofO(n−2)forthemean,uptoanorderofmagnitudefasterthanthetra-
t ditionalGaussianapproximationatthemode.Wealsogivesimilarasymptoticex-
s
[ pansionsformomentsoforder2to4,aswellasexcessKullback-Leiblercost(de-
finedastheadditionalKLcostincurredbyusingEPratherthantheidealGaussian
1
approximation). Alltheseexpansionshighlightthesuperiorconvergenceproper-
v
ties of EP. Our approach for deriving those results is likely applicable to many
7
8 similarapproximateinferencemethods. Inaddition,weintroduceboundsonthe
3 momentsoflog-concavedistributionsthatmaybeofindependentinterest.
2
0
.
1
0
6
Introduction
1
:
v
i
X ExpectationPropagation(EP,1)isanefficientapproximateinferencealgorithmthatisknowntogive
goodapproximations,tothepointofbeingalmostexactincertainapplications[2,3].Itissurprising
r
a that, while the method is empirically very successful, there are few theoretical guarantees on its
behavior. Indeed,mostworkonEPhasfocusedonefficientlyimplementingthemethodinvarious
settings. Theoreticalworkon EPmostly representsnewjustificationsof the methodwhich, while
they offerintuitive insight, do not givemathematicalproofsthat the methodbehavesas expected.
One recent breakthroughis due to Dehaene and Barthelmé [4] who prove that, in the large data-
limit,theEPiterationbehaveslikeaNewtonsearchanditsapproximationisasymptoticallyexact.
However,itremainsunclearhowgoodwecanexpecttheapproximationtobewhenwehaveonly
finitedata.Inthisarticle,weofferacharacterizationofthequalityoftheEPapproximationinterms
oftheworst-casedistancebetweenthetrueandapproximatemeanandvariance.
Whenapproximatingaprobabilitydistributionp(x)thatis,forsomereason,closetobeingGaussian,
anaturalapproximationtouseistheGaussianwithmeanequaltothemode(orargmax)ofp(x)and
withvariancetheinverselog-Hessianatthemode.WecallittheCanonicalGaussianApproximation
(CGA), and its use is usually justified by appealing to the Bernstein-von Mises theorem, which
showsthat,inthelimitofa largeamountofindependentobservations,posteriordistributionstend
towards their CGA. This powerful justification, and the ease with which the CGA is computed
(finding the mode can be done using Newton methods) makes it a good reference point for any
methodlikeEPwhichaimstoofferabetterGaussianapproximationatahighercomputationalcost.
Insection1,weintroducetheCGAandtheEPapproximation.Insection2,wegiveourtheoretical
resultsboundingthequalityofEPapproximations.
1
1 Background
In this section, we present the CGA and give a short introduction to the EP algorithm. In-depth
descriptionsofEPcanbefoundinMinka[5],Seeger[6],Bishop[7],Raymondetal.[8].
1.1 TheCanonicalGaussianApproximation
What we call here the CGA is perhaps the most common approximate inference method in the
machinelearningcookbook. Itisoftencalledthe“Laplaceapproximation”,butthisisamisnomer:
the Laplace approximationrefersto approximatingthe integral p from the integralof the CGA.
ThereasontheCGAissooftenusedisitscompellingsimplicity:´givenatargetdistributionp(x)=
exp(−φ(x)),wefindthemodex⋆ andcomputethesecondderivativesofφatx⋆:
x⋆ = argminφ(x)
β⋆ = φ′′(x⋆)
to form a Gaussian approximation q(x) = N x|x⋆, 1 ≈ p(x). The CGA is effectively just
β⋆
a second-orderTaylor expansion, and its use is(cid:16)justified b(cid:17)y the Bernstein-von Mises theorem [9],
which essentially says that the CGA becomes exact in the large-data (large-n) asymptotic limit.
Roughly,if p (x) ∝ n p(y |x)p (x), where y ...y representindependentdatapoints, then
n i=1 i 0 1 n
lim p (x)=N x|x⋆, 1 intotalvariation.
n→∞ n Q n β⋆
n
(cid:16) (cid:17)
1.2 CGAvsGaussianEP
GaussianEP,asitsnameindicates,providesanalternativewayofcomputingaGaussianapproxima-
tiontoatargetdistribution. ThereisbroadoverlapbetweentheproblemswhereEPcanbeapplied
andtheproblemswheretheCGAcanbeused,withEPcomingatahighercost. Ourcontributionis
toshowformallythatthehighercomputationalcostforEPmaywellbeworthbearing,asEPapprox-
imationscanoutperformCGAsbyanorderofmagnitude. Tobespecific,wefocusonthemoment
estimates(meanandcovariance)computedbyEPandCGA,andderiveboundsontheirdistanceto
thetruemeanandvarianceofthetargetdistribution.Ourboundshaveanasymptoticinterpretation,
andunderthatinterpretationweshowforexamplethatthemeanreturnedbyEPiswithinanorder
of O n−2 of the true mean, wheren is the numberofdatapoints. For the CGA, which uses the
modeasanestimateofthemean,weexhibitaO n−1 upperbound,andwecomputetheerrorterm
(cid:0) (cid:1)
responsibleforthisO n−1 behavior. Thisenablesustoshowthat,inthesituationsinwhichthis
(cid:0) (cid:1)
errorisindeedO n−1 ,EPisbetterthantheCGA.
(cid:0) (cid:1)
(cid:0) (cid:1)
1.3 TheEPalgorithm
We considerthetaskofapproximatingaprobabilitydistributionoverarandom-variableX :p(x),
which we call the targetdistribution. X can be high-dimensional,butfor simplicity, we focuson
theone-dimensionalcase. OneimportanthypothesisthatmakesEPfeasibleisthatp(x)factorizes
intonsimplefactorterms:
p(x)= f (x)
i
i
Y
EPproposestoapproximateeachf (x) (usuallyreferredto as sites) by a Gaussianfunctionq (x)
i i
(referredtoasthesite-approximations). ItisconvenienttousetheparametrizationofGaussiansin
termsofnaturalparameters:
x2
q (x|r ,β )∝exp r x−β
i i i i i
2
(cid:18) (cid:19)
which makes some of the further computationseasier to understand. Note that EP could also be
usedwithotherexponentialapproximatingfamilies. TheseGaussianapproximationsarecomputed
iteratively. Starting from a current approximation(qt(x|rt,βt)), we select a site for update with
i i i
indexi. Wethen:
2
• Computethecavitydistributionqt (x)∝ qt(x). Thisisveryeasyinnaturalparam-
−i j6=1 j
eters:
Q
x2
q (x)∝exp rt x− βt
−i j j 2
j6=i j6=i
X X
• Computethehybriddistributionht(x)∝qt (x)f (x)anditsmeanandvariance
i −i i
• ComputetheGaussianwhichminimizestheKullback-Leiblerdivergencetothehybrid,ie
theGaussianwithsamemeanandvariance:
P(ht)=argmin KL ht|q
i i
q
(cid:0) (cid:0) (cid:1)(cid:1)
• Finally,updatetheapproximationoff :
i
P(ht)
qt+1 = i
i qt
−i
wherethedivisionissimplycomputedasasubtractionbetweennaturalparameters
We iterate these operationsuntila fixedpointis reached,at whichpointwe returna Gaussian ap-
proximationofp(x)≈ q (x).
i
Q
1.4 The“EP-approximation”
Inthiswork,wewillcharacterizethequalityofanEPapproximationofp(x). Wedefinethistobe
anyfixedpointoftheiterationpresentedinsection1.3,whichcouldallbereturnedbythealgorithm.
ItisknownthatEPwillhaveatleastonefixed-point[1],butitisunknownunderwhichconditions
thefixed-pointisunique. Weconjecturethat,whenallsitesarelog-concave(oneofourhypotheses
tocontrolthebehaviorofEP),itisinfactuniquebutwecan’tofferaproofyet. Ifp(x)isn’tlog-
concave, it is straightforwardto constructexamplesin which EPhas multiplefixed-points. These
openquestionswon’tmatterforourresultbecausewewillshowthatallfixed-pointsofEP(should
therebemorethanone)produceagoodapproximationofp(x).
FixedpointsofEPhaveaveryinterestingcharacterization.Ifwenoteq∗thesite-approximationsat
i
a givenfixed-point,h∗ thecorrespondinghybriddistributions,andq∗ theglobalapproximationof
i
p(x),thenthemeanandvarianceofallthehybridsandq∗isthesame1. Aswewillshowinsection
2.2,thisleadstoaverytightboundonthepossiblepositionsofthesefixed-points.
1.5 Notation
We willuserepeatedlythefollowingnotation. p(x) = f (x)isthetargetdistributionwewant
i i
to approximate. The sites f (x) are each approximated by a Gaussian site-approximation q (x)
i i
yieldinganapproximationtop(x)≈q(x)= q (x). TQhehybridsh (x)interpolatebetweenq(x)
i i i
andp(x)byreplacingonesiteapproximationq (x)withthetruesitef (x).
i i
Q
Our results make heavy use of the log-functionsof the sites and the target distribution. We note
φ (x) = −log(f (x)) and φ (x) = −log(p(x)) = φ (x). We will introduce in section 2
i i p i
hypothesesonthesefunctions.Parameterβ controlstheirminimumcurvatureandparametersK
m d
controlthemaximumdth derivative. P
Wewillalwaysconsiderfixed-pointsofEP,wherethemeanandvarianceunderallhybridsandq(x)
isidentical. Wewillnotethesecommonvalues: µ andv . Wewillalsorefertothethirdand
EP EP
fourthcenteredmomentofthehybrids,denotedbymi,mi andtothefourthmomentofq(x)which
3 4
issimply3v2 . Wewillshowhowallthesemomentsarerelatedtothetruemomentsofthetarget
EP
distributionwhichwewillnoteµ,vforthemeanandvariance,andmp,mpforthethirdandfourth
3 4
moment. WealsoinvestigatethequalityoftheCGA:µ≈x⋆ andv ≈ φ′′(x⋆) −1 wherex⋆ isthe
p
themodeofp(x). h i
1Fornon-Gaussianapproximations,theexpectedvaluesofallsufficientstatisticsoftheexponentialfamily
areequal.
3
2 Results
Inthissection,wewillgivetightboundsonthequalityoftheEPapproximation(ie:offixed-points
oftheEPiteration). Ourresultsleanonthepropertiesoflog-concavedistributions[10]. Insection
2.1,weintroducenewboundsonthemomentsoflog-concavedistributions. Theboundsshowthat
those distributions are in a certain sense close to being Gaussian. We then apply these results to
studyfixedpointsofEP,wheretheyenableustocomputeboundsonthedistancebetweenthemean
and variance of the true distribution p(x) and of the approximationgiven by EP, which we do in
section2.2.
Our bounds require us to assume that all sites f (x) are β -strongly log-concave with slowly-
i m
changinglog-function.Thatis,ifwenoteφ (x)=−log(f (x)):
i i
′′
∀i∀xφ (x) ≥ β >0 (1)
i m
∀i∀d∈[3,4,5,6] φ(d)(x) ≤ K (2)
i d
(cid:12) (cid:12)
(cid:12) (cid:12)
The target distribution p(x) then inherits tho(cid:12)se prop(cid:12)erties from the sites. Noting φ (x) =
p
−log(p(x)) = φ (x), then φ is nβ -strongly log-concave and its higher derivatives are
i i p m
bounded:
P
′′
∀x, φ (x) ≥ nβ (3)
p m
∀d∈[3,4,5,6] φ(d)(x) ≤ nK (4)
p d
(cid:12) (cid:12)
(cid:12) (cid:12)
Anaturalconcernhereiswhetherornotourcon(cid:12)ditionso(cid:12)nthesitesareofpracticalinterest.Indeed,
strongly-log-concavelikelihoodsarerare.Wepickedthesestrongregularityconditionsbecausethey
maketheproofsrelativelytractable(althoughstilltechnicalandlong). Theprooftechniquecarries
over to more complicated, but more realistic, cases. One such interesting generalization consists
of the case in which p(x) and all hybridsat the fixed-pointare log-concavewith slowly changing
log-functions (with possibly differing constants). In such a case, while the math becomes more
unwieldy, similar bounds as ours can be found, greatly extending the scope of our results. The
resultswepresenthereshouldthusbeunderstoodasasteppingstoneandnotasthefinalwordon
thequalityoftheEPapproximation:wehavefocusedonprovidingarigorousbutextensibleproof.
2.1 Log-concavedistributionsarestronglyconstrained
Log-concavedistributionshavemanyinterestingproperties. Theyareofcourseunimodal,andthe
familyisclosedunderbothmarginalizationandmultiplication.Forourpurposeshowever,themost
importantpropertyisaresultduetoBrascampandLieb[11],whichboundstheirevenmoments.We
givehereanextensioninthecaseoflog-concavedistributionswithslowlychanginglog-functions
(asquantifiedbyeq. (2)). OurresultsshowthattheseareclosetobeingGaussian.
TheBrascamp-Liebinequalitystatesthat,ifLC(x)∝exp(−φ(x))isβ -stronglylog-concave(ie:
m
′′
φ (x)≥β ),thencenteredevenmomentsofLC areboundedbythecorrespondingmomentsofa
m
Gaussianwithvarianceβ−1. Ifwenotethesemomentsm andµ =E (x)themeanofLC:
m 2k LC LC
m = E (x−µ )2k
2k LC LC
m ≤ (2k−(cid:16)1)!!β−k (cid:17) (5)
2k m
where (2k−1)!! is the doublefactorial: the productof all odd termsfrom 1 to 2k−1. 3!! = 3,
5!! = 15, 7!! = 105, etc. This result can be understoodas stating that a log-concavedistribution
musthaveasmallvariance,butdoesn’tgenerallyneedtobeclosetoaGaussian.
Withourhypothesisofslowlychanginglog-functions,wewereabletoimproveonthisresult. Our
improvedresultsincludeaboundonoddmoments,aswellasfirstorderexpansionsofevenmoments
(eqs. (6)-(9)).
Our extensionto the Brascamp-Liebinequalityis asfollows. If φ is slowlychangingin the sense
′
thatsomeofitshigherderivativesarebounded,aspereq. 2,thenwecangiveaboundonφ (µ )
LC
4
(showingthatµ isclosetothemodex⋆ ofLC,seeeqs. (10)to(13))andm (showingthatLC
LC 3
ismostlysymmetric):
φ′(µ ) ≤ K3 (6)
LC 2β
m
(cid:12) (cid:12)
(cid:12)(cid:12) |m3(cid:12)(cid:12)| ≤ 2βK33 (7)
m
andwecancomputethefirstorderexpansionsofm andm ,andboundtheerrorsintermsofβ
2 4 m
andtheK’s:
m−1−φ′′(µ ) ≤ K32 + K4 (8)
2 LC β2 2β
m m
(cid:12) (cid:12)
φ′′(cid:12)(cid:12)(µ )m −3m (cid:12)(cid:12) ≤ 19K32 + 5K4 (9)
LC 4 2 2 β4 2β3
m m
(cid:12) (cid:12)
(cid:12) (cid:12)
(cid:12) (cid:12)
′′ −1 ′′ −2
With eq. (8) and (9), we see that m ≈ φ (µ ) and m ≈ 3 φ (µ ) and, in that
2 LC 4 LC
′′
sense,thatLC(x)isclosetotheGaussian(cid:16)withmean(cid:17)µ andinverse-v(cid:16)arianceφ (cid:17)(µ ).
LC LC
Theseexpansionscouldbeextendedtofurtherordersandsimilarformulascanbefoundfortheother
momentsofLC(x): forexample,anyoddmomentscanbeboundedby|m | ≤ C K β−(k+1)
2k+1 k 3 m
(with C some constant) and any even moment can be found to have first-order expansion:
k
′′ −k
m ≈ (2k−1)!! φ (µ ) . The proof, as well as more detailed results, can be found in
2k LC
theSupplement. (cid:16) (cid:17)
Note how ourresultrelates to the Bernstein-vonMises theorem, whichsays that, in the limitof a
largeamountofobservations,aposteriorp(x)tendstowardsitsCGA.Ifweconsidertheposterior
obtainedfromnlikelihoodfunctionsthatarealllog-concaveandslowlychanging,ourresultsshow
theslightlydifferentresultthatthemomentsofthatposteriorareclosetothoseofaGaussianwith
mean µ (instead of x⋆ ) and inverse-varianceφ′′(µ ) (instead of φ′′(x⋆ )) . This point is
LC LC LC LC
critical. WhiletheCGAstillendsupcapturingthelimitbehaviorofp,asµ → x⋆ inthelarge-
LC
datalimit(seeeq. (13)below),anapproximationthatwouldreturntheGaussianapproximationat
µ wouldbebetter. ThisisessentiallywhatEPdoes,andthisishowitimprovesontheCGA.
LC
2.2 ComputingboundsonEPapproximations
Inthissection,weconsideragivenEPfixed-pointq∗(x|r ,β )andthecorrespondingapproximation
k i i
ofp(x): q∗(x|r = r ,β = β ). Wewillshowthattheexpectedvalueandvarianceofq∗(resp.
i i
µ andv )areclosetothetruemeanandvarianceofp(resp. µandv),andalsoinvestigatethe
EP EP
qualityoftheCGAP(µ≈x⋆,vP≈ φ′′(x⋆) −1).
p
h i
Under our assumptionson the sites (eq. (1) and (2)), we are able to deriveboundson the quality
of the EP approximation. The proofis quite involvedandlong, andwe will onlypresentit in the
Supplement. Inthe main text, we givea partialversion: we detailthe firststep ofthe demonstra-
tion,whichconsistsofcomputingaroughboundonthedistancebetweenthetruemeanµ, theEP
approximationµ andthemodex⋆,andgiveanoutlineoftherestoftheproof.
EP
Let’sshowthatµ,µ andx⋆ areallclosetooneanother.Westartfromeq. (6)appliedtop(x):
EP
φ′(µ) ≤ K3 (10)
p 2β
m
(cid:12) (cid:12)
(cid:12) (cid:12)
(cid:12) (cid:12)
5
whichtellsusthatφ′(µ)≈0. µmustthusbeclosetox⋆. Indeed:
p
φ′(µ) = φ′(µ)−φ′(x⋆) (11)
p p p
(cid:12)(cid:12) (cid:12)(cid:12) = (cid:12)(cid:12)φ′′(ξ)(µ−x⋆)(cid:12)(cid:12)ξ ∈[µ,x⋆]
(cid:12) (cid:12) (cid:12) p (cid:12)
≥ (cid:12)(cid:12)φ′′(ξ) |µ−x⋆|(cid:12)(cid:12)
(cid:12) p (cid:12)
≥ (cid:12)nβ |µ(cid:12)−x⋆| (12)
(cid:12) m (cid:12)
(cid:12) (cid:12)
Combiningeq. (10)and(12),wefinallyhave:
K
|µ−x⋆|≤n−1 3 (13)
2β2
m
Let’snowshowthatµ isalsoclosetox⋆. Weproceedsimilarly,startingfromeq.(6)butapplied
EP
toallhybridsh (x):
i
∀i φ′(µ )+β µ −r ≤n−1 K3 (14)
i EP −i EP −i 2β
m
(cid:12) (cid:12)
whichisnotreallyequivalent(cid:12)to eq. (10)yet. Recallthat(cid:12)q(x|r,β) hasmeanµEP: we thushave:
(cid:12) (cid:12)
r =βµ . Whichgives:
EP
β µ = ((n−1)β)µ
−i EP EP
!
i
X
= (n−1)r
= r (15)
−i
i
X
Ifwesumalltermsineq. (14),theβ µ andr thuscancel,leavinguswith:
−i EP −i
φ′(µ ) ≤ K3 (16)
p EP 2β
m
(cid:12) (cid:12)
whichisequivalenttoeq.(10)butforµ(cid:12) instead(cid:12)ofµ. Thisshowsthatµ is,likeµ,closetox⋆:
E(cid:12)P (cid:12) EP
K
|µ −x⋆|≤n−1 3 (17)
EP 2β2
m
At this point, we can show that, since they are both close to x⋆ (eq. (13) and (17)), µ = µ +
EP
O n−1 ,whichconstitutesthefirststepofourcomputationofboundsonthequalityofEP.
Af(cid:0)ter co(cid:1)mputingthis, the nextstep is evaluatingthe quality of the approximationof the variance,
viacomputing v−1−v−1 forEPand v−1−φ′′(x⋆) fortheCGA,fromeq. (8). Inbothcases,
EP p
wefind: (cid:12)(cid:12) (cid:12)(cid:12) v−1(cid:12)(cid:12)(cid:12) = v−1 +O(cid:12)(cid:12)(cid:12)(1) (18)
EP
= φ′′(x⋆)+O(1) (19)
p
Since v−1 is of order n, because of eq. (5) (Brascamp-Lieb upper bound on variance), this is a
decentapproximation:therelativeerrorisofordern−1.
WecanfindsimilarlythatbothEPandCGAdoagoodjoboffindingagoodapproximationofthe
fourthmomentofp: m . ForEPthismeansthatthefourthmomentofeachhybridandofq area
4
closematch:
∀im ≈ mi ≈3v2 (20)
4 4 EP
′′ −2
≈ 3 φ (m) (21)
p
(cid:16) (cid:17)
Incontrast,thethirdmomentofthehybridsdoesn’tmatchatallthethirdmomentofp,buttheirsum
does!
m ≈ mi (22)
3 3
i
X
6
Finally,wecomebacktotheapproximationofµbyµ .Theseobeytwoverysimilarrelationships:
EP
φ′(µ)+φ(3)(µ)v = O n−1 (23)
p p 2
φ′(µ )+φ(3)(µ )vEP = O(cid:16)n−1(cid:17) (24)
p EP p EP 2
(cid:16) (cid:17)
Sincev =v +O n−2 (aslightrephrasingofeq. (18)),wefinallyhave:
EP
(cid:0) (cid:1) µ=µ +O n−2 (25)
EP
(cid:0) (cid:1)
Wesummarizetheresultsinthefollowingtheorem:
Theorem1. Characterizingfixed-pointsofEP
Underthe assumptionsgivenby eq. (1) and (2) (log-concavesites with slowly changinglog), we
canboundthequalityoftheEPapproximationandtheCGA:
K
|µ−x∗| ≤ n−1 3
2β2
m
|µ−µ | ≤ B (n)=O n−2
EP 1
v−1−φ′′(x∗) ≤ 2K32 + K4(cid:0) (cid:1)
p β2 2β
m m
(cid:12) (cid:12)
(cid:12) v−1−v−1(cid:12) ≤ B (n)=O(1)
(cid:12) EP(cid:12) 2
(cid:12) (cid:12)
Wegivethefullexpressionforth(cid:12)eboundsB(cid:12)1andB2intheSupplement
Note that the order of magnitude of the bound on |µ−x⋆| is the best possible, because it is at-
tainedforcertaindistributions.Forexample,consideraGammadistributionwithnaturalparameters
(nα,nβ)whosemean α isapproximatedatordern−1 byitsmode α − 1 . Moregenerally,from
β β nβ
eq. (23),wecancomputethefirstorderoftheerror:
φ(3)(µ)v 1 φ(3)(µ)
p p
µ−m≈− ≈− (26)
φ′p′(µ) 2 2 φ′p′(µ) 2
whichis the termcausingthe ordern−1 error. Whenevert(cid:2)histerm(cid:3) issignificant, itis thussafe to
concludethatEPimprovesontheCGA.
Alsonotethat,sincev−1 isofordern,therelativeerrorforthev−1 approximationisofordern−1
for both methods. Despite having a convergencerate of the same order, the EP approximationis
demonstrablybetterthantheCGA,asweshownext.Letusfirstseewhytheapproximationforv−1
isonlyoforder1forbothmethods.Thefollowingrelationshipholds:
v−1 =φ′′(µ)+φ(3)(µ)mp3 +φ(4)(µ)mp4 +O n−1 (27)
p p 2v p 3!v
′′ (cid:0) (cid:1)
Inthisrelationship,φ (µ)isanorderntermwhiletherestareorder1. Ifwenowcomparethisto
p
theCGA approximationofv−1,wefindthatitfailsatmultiplelevels. First,itcompletelyignores
the two order 1 terms, and then, because it takes the value of φ′′ at x⋆ which is at a distance of
p
O n−1 fromµ,itaddsanotherorder1errorterm(sinceφ(3) = O(n)). TheCGAisthusadding
p
quiteabitoferror,evenifeachcomponentisoforder1.
(cid:0) (cid:1)
Meanwhile,v obeysarelationshipsimilartoeq. (27):
EP
v−1 =φ′′(µ )+ φ(3)(µ ) mi3 +φ(4)(µ )3vE2P +O n−1 (28)
EP p EP i EP 2v p EP 3!v
i (cid:20) EP(cid:21) EP
X (cid:0) (cid:1)
′′
WecanseewheretheEPapproximationproduceserrors. Theφ termiswellapproximated:since
p
|µ−µ |=O n−2 ,wehaveφ′′(µ)=φ′′(µ )+O n−1 . Theterminvolvingm isalsowell
EP p p EP 4
(cid:0) (cid:1) (cid:0) (cid:1)
7
approximated,andwecanseethattheonlytermthatfailsisthem term. Theorder1erroristhus
3
entirelycomingfromthisterm,whichshowsthatEPperformancesuffersmorefromtheskewness
ofthetargetdistributionthanfromitskurtosis.
Finally,notethat,withourresult,wecangetsomeintuitionsaboutthequalityoftheEPapproxima-
tionusingothermetrics.Forexample,ifthemostinterestingmetricistheKLdivergenceKL(p,q),
the excess KL divergence from using the EP approximation q instead of the true minimizer q
KL
(whichhasthesamemeanµandvariancevasp)isgivenby:
q (x−µ)2 (x−µ )2 1 v
∆KL= plog KL = p(x) − + EP − log (29)
ˆ q ˆ 2v 2v 2 v
EP (cid:18) EP(cid:19)!
1 v v (µ−µ )2
= −1−log + EP (30)
2 v v 2v
(cid:20) EP (cid:18) EP(cid:19)(cid:21) EP
1 v−v 2 (µ−µ )2
EP EP
≈ + (31)
4 v 2v
(cid:18) EP (cid:19) EP
whichwerecognizeasKL(q ,q). AsimilarformulagivestheexcessKLdivergencefromusing
KL
theCGAinsteadofq . Forbothmethods,thevariancetermisofordern−2 (thoughitshouldbe
KL
smallerforEP),butthemeantermisofordern−3forEPwhileitisofordern−1fortheCGA.Once
again,EPisfoundtobethebetterapproximation.
Finally,notethatourboundsarequitepessimistic:thetruevaluemightbeamuchbetterfitthanwe
havepredictedhere.
A first cause is the bounding of the derivatives of log(p) (eqs. (3),(4)): while those bounds are
correct,theymightprovetobeverypessimistic. Forexample,ifthecontributionsfromthesitesto
thehigher-derivativescanceleachotherout,amuchlowerboundthannK mightapply. Similarly,
d
theremightbeanotherlowerboundonthecurvaturemuchhigherthannβ .
m
Anothercauseistheboundingofthevariancefromthecurvature. WhileapplyingBrascamp-Lieb
requiresthe distributionto havehighlog-curvatureeverywhere,a distributionwith high-curvature
close to the mode and low-curvature in the tails still has very low variance: in such a case, the
Brascamp-Liebboundisverypessimistic.
Inordertoimproveonourbounds,wewillthusneedtousetighterboundsonthelog-derivativesof
thehybridsandofthetargetdistribution,butwewillalsoneedanextensionoftheBrascamp-Lieb
result that can deal with those cases where a distribution is strongly log-concavearoundits mode
but,inthetails,thelog-curvatureismuchlower.
3 Conclusion
EPhasbeenused fornowquitesome time withoutanytheoreticalconcreteguaranteesonits per-
formance.Inthiswork,weprovideexplicitperformanceboundsandshowthatEPissuperiortothe
CGA, in the sense of giving provablybetter approximationsof the mean and variance. There are
nowtheoreticalargumentsforsubstitutingEPtotheCGAinanumberofpracticalproblemswhere
thegaininprecisionisworththeincreasedcomputationalcost. Thisworktackledthefirststepsin
provingthatEPoffersanappropriateapproximation. Continuinginitstrackswillmostlikelylead
to more generaland less pessimistic bounds, but it remainsan open question how to quantify the
qualityoftheapproximationusingotherdistance measures. Forexample,itwouldbehighlyuse-
fulformachinelearningif onecouldshowboundsonpredictionerrorwhenusingEP.We believe
thatourapproachshouldextendtomoregeneralperformancemeasuresandplantoinvestigatethis
furtherinthefuture.
References
[1] ThomasP.Minka. ExpectationPropagationforapproximateBayesianinference. InUAI’01:
Proceedingsofthe 17thConferencein Uncertainty in ArtificialIntelligence, pages362–369,
San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc. ISBN 1-55860-800-1.
URLhttp://portal.acm.org/citation.cfm?id=720257.
8
[2] Malte Kuss and Carl E. Rasmussen. Assessing ApproximateInferencefor Binary Gaussian
ProcessClassification. J.Mach.Learn.Res.,6:1679–1704,December2005.ISSN1532-4435.
URLhttp://portal.acm.org/citation.cfm?id=1194901.
[3] Hannes Nickisch and Carl E. Rasmussen. Approximations for Binary Gaussian Process
Classification. Journal of Machine Learning Research, 9:2035–2078, October 2008. URL
http://www.jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf.
[4] Guillaume Dehaene and Simon Barthelmé. Expectation propagation in the large-data limit.
Technicalreport,March2015. URLhttp://arxiv.org/abs/1503.08060.
[5] T. Minka. Divergence Measures and Message Passing. Technical report, 2005. URL
http://research.microsoft.com/en-us/um/people/minka/papers/message-passing/minka-
[6] M.Seeger. ExpectationPropagationforExponentialFamilies. Technicalreport,2005. URL
http://people.mmci.uni-saarland.de/~{}mseeger/papers/epexpfam.pdf.
[7] Christopher M. Bishop. Pattern Recognition and Machine Learn-
ing (Information Science and Statistics). Springer, 1st ed. 2006. corr.
2nd printing 2011 edition, October 2007. ISBN 0387310738. URL
http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0387310738
[8] Jack Raymond, Andre Manoel, and Manfred Opper. Expectation propagation, September
2014. URLhttp://arxiv.org/abs/1409.6179.
[9] Anirban DasGupta. Asymptotic Theory of Statistics and Probability (Springer
Texts in Statistics). Springer, 1 edition, March 2008. ISBN 0387759700. URL
http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0387759700
[10] Adrien Saumard and Jon A. Wellner. Log-concavity and strong log-concavity:
A review. Statist. Surv., 8:45–114, 2014. doi: 10.1214/14-SS107. URL
http://dx.doi.org/10.1214/14-SS107.
[11] Herm J. Brascamp and Elliott H. Lieb. Best constants in young’s inequality, its con-
verse, and its generalization to more than three functions. Advances in Mathematics, 20
(2):151–173, May 1976. ISSN 00018708. doi: 10.1016/0001-8708(76)90184-5. URL
http://dx.doi.org/10.1016/0001-8708(76)90184-5.
9
Supplementary information of “Bounding
errors of Expectation-Propagation”
A Improving ontheBrascamp-Lieb bound
Inthis section, we detailourmathematicalresultsconcerningthe extensionofthe Brascamp-Lieb
bound.
We will note LC(x) = exp(−φ(x)) a log-concave distribution. We assume that φ is strongly
convex,andslowlychanging,ie:
′′
∀xφ (x) ≥ β (32)
m
∀d∈[3,4,5,6] φ(d)(x) ≤ K (33)
d
(cid:12) (cid:12)
(cid:12) (cid:12)
A.1 TheoriginalBrascamp-Liebtheorem (cid:12) (cid:12)
Let µ = E (x) be the expected value of LC. The original Brascamp-Lieb result [1976]
LC LC
concernsboundingfractionalcenteredmomentsofLC bythecorrespondingfractionalmomentsof
aGaussianofvarianceβ−1, centeredatµ . Notingg(x) = N x|µ ,β−1 thatGaussian,we
m LC LC m
have:
∀α≥1E (|x−µ |α)≤E (|x−µ(cid:0) |α) (cid:1) (34)
LC LC g LC
However,we arenotinterestedin theirfullresult, butonlyin arestrictedversionofitwhichonly
concernsevenmoments.Thisversionsimplyreads:
∀k ∈Nm =E |x−µ |2k ≤ (2k−1)m β−1 (35)
2k LC LC 2k−2 m
(cid:16) m(cid:17) ≤ (2k−1)!!β−k (36)
2k m
where (2k−1)!! is the double-factorial: the productof all odd terms between 1 and 2k−1. Eq.
(35) might be a new result. Note that equality only occurs when f(x) = 1 and LC is Gaussian.
Notealsothattheboundsonthehigherderivativesofφarenotneededforthisresult,butonlyfor
ourextension.
We offerherea proofofeq. (35)(fromwhicheq. (36)isa trivialconsequence),whichisslightly
differentfromBrascamp&Lieb’soriginalproof. Webelievethisprooftobeoriginal,thoughitis
stillquitesimilartotheoriginalproof.
Proof. Let’sdecomposeLC(x)intotwoparts:
• g(x)=N x|µ ,β−1 theboundingGaussianwithsamemeanasLC
LC m
• f(x)= LC(cid:0)(x) theremai(cid:1)nder
g(x)
f iseasilyshowntobelog-concave,whichmeansthatitisunimodal. Wewillnotex⋆ themodeof
f. f isincreasingon]−∞,x⋆]anddecreasingon[x⋆,∞[. Wethusknowthesignoff′(x):
sign f′(x) =sign(x⋆−x) (37)
(cid:16) (cid:17)
+∞ ′
Considertheintegral: g(x)f (x)dx. Byintegrationbyparts(orbyStein’slemma),wehave:
−∞
´
+∞ +∞
′
g(x)f (x)dx = g(x)f(x)β (x−µ)dx
ˆ ˆ m
−∞ −∞
= β (µ−µ)
m
+∞
′
g(x)f (x)dx = 0 (38)
ˆ
−∞
10
