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Focused Belief Propagation for Query-Specific Inference. Anton Chechetka Carlos Guestrin. 14 May 2010. Query-Specific inference problem. q uery . known. n ot interesting. Using information about the query to speed up convergence of belief propagation for the query marginals.

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focused belief propagation for query specific inference

Focused Belief Propagation for Query-Specific Inference

Anton ChechetkaCarlos Guestrin

14 May 2010

query specific inference problem
Query-Specific inference problem

query

known

not interesting

Using information about the queryto speed up convergence of belief propagation

for the query marginals

graphical models
Graphical models
  • Talk focus: pairwise Markov random fields
  • Paper:arbitrary factor graphs (more general)
  • Probability is a product of local potentials
  • Graphical structure
  •  Compact representation
  •  Intractable inference
    •  Approximate inference often works well in practice

potential

k

r

h

i

variable

j

s

u

loopy belief propagation
(loopy) Belief Propagation
  • Passing messages along edges
  • Variable belief:
  • Update rule:
  • Result: all single-variable beliefs

k

r

h

i

j

s

u

loopy belief propagation1
(loopy) Belief Propagation
  • Update rule:
  • Message dependencies are local:
  • Round–robin schedule
    • Fix message order
    • Apply updates in that order until convergence

dependence

k

r

h

i

dep.

j

s

u

dependence

dynamic update prioritization
Dynamic update prioritization

large change

small change

  • Fixed update sequence is not the best option
  • Dynamic update scheduling can speed up convergence
    • Tree-Reweighted BP [Wainwright et. al., AISTATS 2003]
    • Residual BP [Elidan et. al. UAI 2006]
  • Residual BP  apply the largest change first

large change

small change

1

2

large change

small change

informative update

wasted computation

residual bp elidan et al uai 2006
Residual BP [Elidan et. al., UAI 2006]
  • Update rule:
  • Pick edge with largest residual
  • Update

new

old

More effort on the difficult parts of the model 

But no query 

our contributions
Our contributions
  • Using weighted residuals to prioritize updates
  • Define message weights reflecting the importance of the message to the query
  • Computing importance weights efficiently
  • Experiments: faster convergence on large relational models
why edge importance weights
Why edge importance weights?

query

residual < residualwhich to update??

  • Residual BP updates
  • no influence on the query
  • wasted computation
  • want to update
  • influence on the queryin the future

Our work  max approx. eventual effect on P(query)

Residual BP  max immediate residual reduction

query specific bp
Query-Specific BP
  • Update rule:
  • Pick edge with
  • Update

new

old

edgeimportance

the only change!

Rest of the talk: defining and computing edge importance

edge importance base case
Edge importance base case
  • Pick edge with

approximate eventual update effect on P(Q)

query

Base case: edge directly connected to the queryAji=??

k

r

h

i

j

s

u

change in query belief

change in message

||P(NEW)(Q)  P(OLD)(Q)||

||m(NEW)  m(OLD)||

1

1

ji

ji

||m||

||P(Q)||

tight bound

ji

edge importance one step away
Edge importance one step away

query

mji

mji

Edge one step away from the query: Arj=??

sup

sup

mrj

mrj

||P(Q)||

||m ||

k

r

over values ofall other messages

h

ji

i

change in query belief

j

s

u

||m|| 

rj

change in message

message importance

can compute in closed formlooking at only fji [Mooij, Kappen; 2007]

edge importance general case
Edge importance general case

Base case: Aji=1

k

r

h

query

i

One step away:

Arj=

j

s

u

mji

sup

mrj

Generalization?

  • expensive to compute
  • bound may be infinite

||P(Q)||

||msh||

P(Q)

sup

msh

P(Q)

mhr

mrj

mji

sup

sup

sup

sup

msh

msh

mrj

mhr

sensitivity(): max impact along the path 

edge importance general case1
Edge importance general case

2

1

query

k

r

h

sensitivity(): max impact along the path 

i

P(Q)

mhr

mrj

mji

j

s

u

sup

sup

sup

sup

msh

  • Ash= max all paths from to query sensitivity()

msh

mhr

mrj

h

There are a lot of paths in a graph,trying out every one is intractable 

efficient edge importance computation
Efficient edge importance computation

There are a lot of paths in a graph,trying out every one is intractable 

always decreases as the path grows

Dijkstra’s(shortest paths) alg. will efficiently find max-sensitivity pathsfor every edge 

always 1

always 1

always 1

sensitivity( hrji) =

decomposes into individual edge contributions

mhr

mrj

mji

sup

sup

sup

mhr

msh

mrj

  • A= max all paths from to query sensitivity()
query specific bp1
Query-Specific BP
  • Run Dijkstra’salgstarting at query to get edge weights
  • Pick edge with largest weighted residual
  • Update

Aji= max all paths from itoquery sensitivity()

More effort on the difficult parts of the model

and relevant

Takes into account not only graphical structure, but also strength of dependencies

outline
Outline
  • Using weighted residuals to prioritize updates
  • Define message weights reflecting the importance of the message to the query
  • Computing importance weights efficiently
    • As an initialization step before residual BP
  • Restore anytime behavior of residual BP
  • Experiments: faster convergence on large relational models
big picture
Big picture

Before

After

?

?

long initialization

all marginals

all marginals

anytimeproperty

Dijkstra

BP

query marginals

all marginals

This is broken!

interleaving bp and dijkstra s
Interleaving BP and Dijkstra’s
  • Dijkstra’sexpands the highest weight edges first
    • Can pause it at any time and get the most relevant submodel

query

full model

Dijkstra

Dijkstra

When to stop BP and resume Dijkstra’s?

BP

BP

BP

Dijkstra

interleaving
Interleaving
  • Dijkstra’s expands the highest weight edges first

query

expanded onprevious iteration

not yet expanded

just expanded

minexpanded edgesA  A

suppose

upper bound on priority

actual priority of

 M  minexpanded A

no need to expand further at this point

any time query specific bp
Any-Time query-specific BP

query

Query-specific BP:

Dijkstra’s alg.

BP updates

k

r

same BP update sequence!

i

j

s

Anytime QSBP:

experiments the setup
Experiments – the setup
  • Relational model: semi-supervised people recognition in collection of images [with Denver Dash and MatthaiPhilipose @ Intel]
    • One variable per person
    • Agreement potentials for people who look alike
    • Disagreement potentials for people in the same image
    • 2K variables, 900K factors
  • Error measure: KL from the fixed point of BP

agree

disagree

experiments convergence speed
Experiments – convergence speed

AnytimeQuery-Specific BP(with Dijkstra’s interleaving)

Residual BP

Query-Specific BP

better

conclusions
Conclusions
  • Prioritize updates by weighted residuals
    • Takes query info into account
  • Importance weights depend on both the graphical structure and strength of local dependencies
  • Efficient computation of importance weights
  • Much faster convergence on large relational models

Thank you!

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