Practical Probabilistic Relational Learning. Sriraam Natarajan. TakeAway 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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Sriraam Natarajan
Learn from rich, highly structured data!
Data is i.i.d.
Burglary
Earthquake
+
Alarm
MaryCalls
JohnCalls
Attributes(Features)
Data
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
Statistical Relational Learning (SRL)
Add Probabilities
Logic
Add Relations
Probabilities
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
How do weactuallylearn relational modelsfromdata?
To predict heartAttack(X)
male(X)
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
Data
Residuals
=

Data
Induce
+
Predictions
Loss fct
+
Initial Model
Iterate
+
+
Final Model =
+
+
+
+
…
Predicting the advisor for a student
Movie Recommendation
Citation Analysis
Machine Reading
Statistical Relational
Parallel
Scales well, stochastic gradients, online learning, …
Symmetries, compact models, lifted inference, ….
Symmetries, compact models, lifted inference, ….
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)
Generate initial tree pieces and variablize its arguments.