Practical probabilistic relational learning
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Practical Probabilistic Relational Learning. Sriraam Natarajan. Take-Away Message. Learn from rich, highly structured data!. Traditional Learning. Data is i.i.d. Burglary. Earthquake. +. Alarm. MaryCalls. JohnCalls. Attributes(Features). Data. Learning. Earthquake. Burglary.

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Practical Probabilistic Relational Learning

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Practical probabilistic relational learning

Practical Probabilistic Relational Learning

Sriraam Natarajan


Take away message

Take-Away Message

Learn from rich, highly structured data!


Traditional learning

Traditional Learning

Data is i.i.d.

Burglary

Earthquake

+

Alarm

MaryCalls

JohnCalls

Attributes(Features)

Data


Learning

Learning

Earthquake

Burglary

Alarm

JohnCalls

MaryCalls


Real world problem predicting adverse drug reactions

Real-World Problem: Predicting Adverse Drug Reactions

PatientID Date Physician Symptoms Diagnosis

P1 1/1/01 Smith palpitations hypoglycemic

P1 2/1/03 Jones fever, aches influenza

PatientIDGenderBirthdate

P1 M 3/22/63

Visit Table

Patient Table

PatientID Date Lab Test Result

PatientID SNP1 SNP2 … SNP500K

P1 AA AB BB

P2 AB BB AA

P1 1/1/01 blood glucose 42

P1 1/9/01 blood glucose 45

SNP Table

Lab Tests

PatientID Date Prescribed Date Filled Physician Medication Dose Duration

P1 5/17/98 5/18/98 Jones prilosec 10mg 3 months

Prescriptions


Logic probability probabilistic logic aka statistical relational learning models

Logic + Probability = Probabilistic Logic aka Statistical Relational Learning Models

Statistical Relational Learning (SRL)

Add Probabilities

Logic

Add Relations

Probabilities

  • Several previous SRL Workshops in the past decade

  • This year – StaRAI @ AAAI 2013


Practical probabilistic relational learning

Classical Machine

Learning

Statistical Relational Learning

Probability Theory

Probabilistic Logic

Stochastic

Deterministic

Prop Rule

Learning

Inductive Logic Programming

Learning

First Order Logic

Propositional

Logic

No Learning

Prop

FO


Costs and benefits of the srl soup

Costs and Benefits of the SRL soup

  • Benefits

    • Rich pool of different languages

    • Very likely that there is a language that fits your task at hand well

    • A lot research remains to be done, ;-)

  • Costs

    • “Learning” SRL is much harder

    • Not all frameworks support all kinds of inference and learning settings

How do weactuallylearn relational modelsfromdata?


Why is this problem hard

Why is this problem hard?

  • Non-convex problem

  • Repeated search of parameters for every step in induction of the model

  • First-order logic allows for different levels of generalization

  • Repeated inference for every step of parameter learning

    • Inference is P# complete

  • How can we scale this?


Relational probability trees

Relational Probability Trees

To predict heartAttack(X)

male(X)

  • Each conditional probability distribution can be learned as a tree

  • Leaves are probabilities

  • The final model is the set of the RRTs

yes

no

chol(X,Y,L), Y>40,L>200

yes

no

diag(X,Hypertension,Z),Z>55

0.8

no

yes

bmi(X,W,55), W>30

0.05

[Blockeel & De Raedt ’98]

no

yes

0.3

0.77


Gradient tree boosting friedman annals of statistics 29 5 1189 1232 2001

Gradient (Tree) Boosting[Friedman AnnalsofStatistics 29(5):1189-1232, 2001]

  • Models = weighted combination of a large number ofsmalltrees (models)

  • Intuition: Generate an additive model by sequentially fitting small trees to pseudo-residuals from a regression at each iteration…

Data

Residuals

=

-

Data

Induce

+

Predictions

Loss fct

+

Initial Model

Iterate

+

+

Final Model =

+

+

+

+


Boosting results mlj 11

Boosting Results – MLJ 11

Predicting the advisor for a student

Movie Recommendation

Citation Analysis

Machine Reading


Other applications

Other Applications

  • Similar Results in several other problems

  • Imitation Learning – Learning how to act from demonstrations (Natarajan et al IJCAI ‘11)

    • Robocup, a grid world domain, traffic signal domain and blocksworld

  • Prediction of CAC Levels – Predicting cardio-vascular risks in young adults (Natarajan et al – IAAI 13)

  • Prediction of heart attacks (Weiss et al – IAAI 12, AI Magazine 12)

  • Prediction of onset of Alzheimer’s (Natarajan et al ICMLA ’12, Natarajan et al IJMLC 2013)


Parallel lifted learning

Parallel Lifted Learning


Practical probabilistic relational learning

Stochastic ML

Statistical Relational

Parallel

Scales well, stochastic gradients, online learning, …

Symmetries, compact models, lifted inference, ….

Symmetries, compact models, lifted inference, ….


Symmetry based inference

Symmetry based inference


Practical probabilistic relational learning

2

1

1

1

2

2

3

4

3

3

4

4

5

5

5

root clause

Tree (set of clauses)

P(Anna)

HI (Bob)

P(Anna)  !P(Bob)

P(Anna)!P(Bob)

P(Bob)=> HI(Bob)

P(Bob)=> !HI(Anna)

1

neighboring clauses

P(Anna) => !HI(Bob)

2

3

4

Variabilized tree

P(Anna) => HI(Anna)

P(X)!P(Y)

P(Y)=> HI(Y)

P(Y)=> !HI(X)

P(Bob) => HI(Bob)

5

HI(Anna)

P(Bob) => !HI(Anna)

P(Bob)


Lifted training

Lifted Training

Generate initial tree pieces and variablize its arguments.


Challenges

Challenges

  • Message schedules

  • Iterative Map-reduce?

  • How do we take this idea to learning the models?

  • How can we more efficiently parallelize symmetry identification?

  • What are the compelling problems? Vision, NLP,…


Conclusion

Conclusion

  • The world is inherently relational and uncertain

  • SRL has developed into an exciting field in the past decade

    • Several previous SRL workshops

  • Boosting Relational models has promising initial results

    • Applied to several different problems

  • First scalable relational learning algorithm

  • How can we parallelize/scale this algorithm?

  • Can this benefit from an inference algorithm like Belief Propagation that can be parallelized easily?


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