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Variational Inference Amr Ahmed Nov. 6 th 2008

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Outline Approximate Inference Variational inference formulation – Mean Field Examples – Structured VI Examples

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Approximate Inference Exact inference is exponential in clique size Posterior is highly peaked – Can be approximated by a simpler distribution Formulate inference as an optimization problem – Define an objective: how good is Q – Define a family of simpler distributions to search over – Find Q* that best approximate P All Distributions P Q* Tractable family

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Approximate Inference Exact inference is exponential in clique size Posterior inference is highly peaked – Can be approximated by a simpler distribution Formulate inference as an optimization problem – Define an objective: how good is Q – Define a family of simpler distributions to search over – Find Q* that best approximate P Today we will cover variational Inference – Just a possible way of such a formulation There are many other ways – Variants of loopy BP (later in the semester)

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What is Variational Inference? Difficulty SATGrade Happy Job Coherence Letter Intelligence P Posterior “hard” to compute We know the CPTs (factors) Difficulty SATGrade Happy Coherence Letter Intelligence Q Tractable Posterior Family Set CPT (factors) Get distribution, Q Fully instantiated distribution Q* Enables exact Inference Has low tree-width Clique tree, variable elimination, etc.

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VI Questions Which family to choose – Basically we want to remove some edges Mean field: fully factorized Structured : Keep tractable sub-graphs How to carry the optimization Assume P is a Markov network – Product of factors (that is all)

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Outline Approximate Inference Variational inference formulation – Mean Field Examples – Structured VI Examples

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Mean-field Variational Inference Difficulty SATGrade Happy Job Coherence Letter Intelligence P Posterior “hard” to compute We know the CPTs (factors) Difficulty SATGrade Happy Coherence Letter Intelligence Q Tractable Posterior Family Set CPT (factors) Get distribution, Q Fully instantiated distribution Q* Enables exact Inference

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10-708 – Carlos Guestrin 2006-2008 9 D(q||p) for mean field – KL the reverse direction: cross-entropy term p: q:

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The Energy Functional Theorem : Where energy functional: Our problem now is Minimize Maximize You get Lower bound on lnZ Maximizing F[P,Q] tighten the bound And gives better prob. estimates Tractable By construction

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Our problem now is Theorem: Q is a stationary point of mean field approximation iff for each j: Tractable By construction The Energy Functional

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Outline Approximate Inference Variational inference formulation – Mean Field Examples – Structured VI Examples

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 - Given the factors in P, we want to get the factors for Q. - Iterative procedure. Fix all q -i, compute q i via the above equation - Iterate until you reach a fixed point

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 In your homework X1X1

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 X1X1 -q(X 6 ) get to be tied with q(x i ) for all xi that appear in a factor with it in P -i.e. fixed point equations are tied according to P - What q(x 6 ) gets is expectations under these q(x i ) of how the factor looks like - can be somehow interpreted as message passing as well (but we won’t cover this) Intuitively

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Example X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : fully factorized MN X2X2 X5X5 X6X6 X7X7 X 10 -q(X 6 ) get to be tied with q(x i ) for all xi that appear in a factor with it in P -i.e. fixed point equations are tied according to P - What q(x 6 ) gets is expectations under these q(x i ) of how the factor looks like - can be somehow interpreted as message passing as well (but we won’t cover this) Intuitively

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Outline Approximate Inference Variational inference formulation – Mean Field Examples – Structured VI Examples Inference in LDA (time permits)

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Structured Variational Inference Difficulty SATGrade Happy Job Coherence Letter Intelligence P Posterior “hard” to compute We know the CPTs (factors) Difficulty SATGrade Happy Coherence Letter Intelligence Q Tractable Posterior Family Set CPT (factors) Get distribution, Q Fully instantiated distribution Q* Enables exact Inference Has low tree-width You don’t have to remove all edges Just keep tractable sub graphs: trees, chain

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Structured Variational Inference X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : tractably factorized MN Given factors in P, get factors in Q that minimize energy functional Goal Still tractable By construction X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12

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Fixed point Equations X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : tractably factorized MN Factors that scope() is not independent of C j in Q X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12

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Fixed point Equations X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 Factors that scope() is not independent of C j in Q What are the factors in Q that interact with x1,x5

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Fixed point Equations X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 What are the factors in P with scope not separated from x1,x5 in Q - First, get variables that can not be separated form x 1 and x 5 in Q - Second collect the factors they appear in in P

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X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 What are the factors in P with scope not separated from x1,x5 in Q - First, get variables that can not be separated form x 1 and x 5 in Q -Second collect the factors they appear in in P - Expectation is taken over Q(scope[factor] | c j ) Fixed point Equations

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P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 B = 59 A = 15 59 12 56 9,10 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 Fixed point Equations

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P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 A = 59 B = 15 59 12 56 9,10 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 Fixed point Equations

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P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 Fixed point Equations

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P: Pairwise MN Q : tractably factorized MN X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 X 11 X 12 -What have we picked from P when dealing with the first factor (X1,x5)? -Factors that interact with (x1,x5) in both P and Q - Fixed point equations are tied in the same way these variables are tied in P and Q - can be also interpreted as passing message over Q of expectations on these factors - Q: how to compute these madrigals in Q efficiently? Homework! Some intuition

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Variational Inference in Practice Highly used – Fast and efficient – Approximation quality might be not that good Always peaked answers (we are using the wrong KL) Always captures modes of P Spread of P is usually lost – Famous applications of VI VI for topic models (LDA) – You can derive LDA equations in few lines using the fully factorized update equations (exercise) – We might go over it in a special recitations but try it if you are doing a project in topic models Involved temporal models Many more…

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