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Transcript of Presenter: Russell Greiner. 2 Helping the world understand data … and make informed decisions....
![Page 1: Presenter: Russell Greiner. 2 Helping the world understand data … and make informed decisions. Single decision: determine class label of an instance set.](https://reader038.fdocuments.us/reader038/viewer/2022110304/551ac9235503466b6a8b50dd/html5/thumbnails/1.jpg)
Learning to Learning to PredictPredict
Presenter: Russell GreinerPresenter: Russell Greiner
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Vision StatementVision Statement
Helping the world understand data
… and make informed decisions.… and make informed decisions.
Single decision:determineclass label of an instance
set of labels of set of pixels, …
value of a property of an instance, …
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Motivation for Training a Motivation for Training a PredictorPredictor
TempPress.
Sore-Throat
… Color
32 90 N … PalePredictor
treatX
Ok
Need to know “label” of an instance,to determine appropriate actionPredictorMed( patient#2 ) =? “treatX is Ok”
Unfortunately, Predictor( . ) not known a priori
But many examples of patient, treatX
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Motivation for Training a Motivation for Training a PredictorPredictorMachine learning provide alg’s for mapping
{ patient, treatX } to Predictor(.) function
Pale…N9032
Color…Sore-
ThroatPress.Temp Predictor
treatX
Ok
Learner
N
N
Y
Sore Throat
…
…
…
…
NoPale8710
::::
OkClear11022
NoPale9535
treatXColourPress.Temp.
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Motivation for Training a PredictorMotivation for Training a Predictor
Need to learn (not program it in) when predictor is … … not known… not expressible… changing… user dependent
Pale…N9032
Color…Sore-Throat
Press.Temp PredictortreatX
No
Learner
N
N
Y
Sore Throat
…
…
…
…
NoPale8710
::::
OkClear11022
NoPale9535
treatXColourPress.Temp.
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PersonnelPersonnelPI synergy:
Greiner, Schuurmans, Holte, Sutton, Szepesvari, Goebel
5 Postdocs16 Grad students (5 MSc, 11 PhD)5 Supporting technical staff
+ personnel for Bioinformatics thrust
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Partners/CollaboratorsPartners/Collaborators
4 UofA CS profs1 UofAlberta Math/StatNon-UofA collaborators:
Google, Yahoo!, Electronic Arts, UofMontreal, UofWaterloo, UofNebraska, NICTA, NRC-IIT, …
+ Bioinformatics thrust collaborators
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Additional ResourcesAdditional ResourcesGrants
$225K CFI$100K MITACS$100K Google
Hardware68 processor, 2TB, Opteron Cluster54 processor, dual core, 1.5TB, Opteron
Cluster
+ funds/data for Bioinformatics thrust
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HighlightsHighlights
IJCAI 2005 – Distinguished Paper Prize
UM 2003 – Best Student Paper PrizeWebIC technology is foundation for
start-up companySignificant advances in extending
SVMs to use Un-supervised/Semi-supervised data, and for structured data
+ Highlights from Bioinformatics thrust
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re: training dataIID / unstructuredLots of instancesLow dimensions Complete featuresCompletely labeledBalanced data is sufficient
Pale…N9032
Color…Sore-
ThroatPress.
Temp PredictortreatX
No
Learner
N
N
Y
Sore Throat
…
…
…
…
NoPale8710
::::
OkClear11022
NoPale9535
treatXColourPressTemp.
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re: training dataSimplifying assumptions re: training dataIID / unstructured ?Lots of instancesLots of instancesLow dimensions Low dimensions Complete featuresComplete featuresCompletely labeledCompletely labeledBalanced dataBalanced data is sufficientis sufficient
Segmenting Brain Tumors
Extensions to Conditional Random Fields, …
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instances ?Low dimensions ?Complete featuresComplete featuresCompletely labeledCompletely labeledBalanced dataBalanced data is sufficientis sufficient
m 1000’s
7.3 2.1 5.0 … 1.1 Y
22.1 6.03 3.1 … 3.0 Y
22.1 6.03 3.1 … 3.0 Y
22.1 6.03 3.1 … 3.0 Y
22.1 6.03 2.2 … 3.0 Y
22.1 6.03 12 … 3.0 Y
22.1 6.03 5 … 3.0 Y
: : : : :
32.0 1.9 5.8 … 2.8 N
N 10’s
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instances ?Low dimensions ?Complete featuresComplete featuresCompletely labeledCompletely labeledBalanced dataBalanced data is sufficientis sufficient
g1 g2 g3 … gN disease
7.3 2.1 55.0 … 1.1 Y
22.1 6.03 29.1 … 3.0 Y
: : : : :
32.0 1.9 15.8 … 2.8 N
m100
N 20,000
Microarray, SNP Chips, …
Dimensionality Reduction… L 2 Model: Component Discovery BiCluster Coding
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instancesLots of instancesLow dimensions Low dimensions Complete features ?Completely labeledCompletely labeledBalanced dataBalanced data is sufficientis sufficient
g1 g2 g3 … gN diseaseX
7.3 2.1 55.0 … 1.1 Y
22.1 6.03 29.1 … 3.0 Y
: : : : :
32.0 1.9 15.8 … 2.8 N
Budget Learning
g1 g2 g3 … gN diseaseX
7.3 2.1 55.0 … 1.1 Y
22.1 … 3.0 Y
: : : : :
1.9 … N
g1 g2 g3 … gN diseaseX
Y
Y
:
N
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instancesLots of instancesLow dimensions Low dimensions Complete featuresComplete featuresCompletely labeled ?Balanced dataBalanced data is sufficientis sufficient
g1 g2 g3 … gN treatX
7.3 2.1 55.0 … 1.1 Y
22.1 6.03 29.1 … 3.0 Y
20.7 6.03 29.1 … 3.0 N
22.1 8.73 20.1 … 5.0 N
123 6.03 17.1 … 7.0 Y
: : : : :
32.0 1.9 15.8 … 2.8 N
SemiSupervised LearningActive Learning
treatX
Y
Y
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instancesLots of instancesLow dimensions Low dimensions Complete featuresComplete featuresCompletely labeledCompletely labeledBalanced data ? is sufficientis sufficient
Cost Curves (analysis)
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Learning to Predict: Learning to Predict: ChallengesChallenges
Simplifying assumptions re training dataSimplifying assumptions re training dataIID / unstructuredIID / unstructuredLots of instancesLots of instancesLow dimensions Low dimensions Complete featuresComplete featuresCompletely labeledCompletely labeledBalanced dataBalanced data is sufficient ?
Robust SVMMixture Using VarianceLarge Margin Bayes NetCoordinate Classifier…
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Projects and StatusProjects and StatusStructured Prediction
Random Fields Parsing Unsupervised M3N
Dimensional Reduction (L 2 Model: Component Discovery)
Budgeted LearningSemiSupervised Learning
large-margin (SVM) probabilistic (CRF) graph based transduction
Active Learning CostCurvesRobust SVMCoordinated ClassifiersMixture Using VarianceLarge Margin Bayes Net
IID / unstructured
Lots of instancesLow dimensions Complete features
Completely labeled
Balanced data
Beyond simple learners
Poster # 26
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Budgeted LearningBudgeted Learning
Technical DetailsTechnical Details
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b 0 5 b 1
b 1 3 a 0
a 1 1 a 0
b 1 1 a 0
a 0 3 a 1
Person 1
Person 2
Typical Supervised LearningTypical Supervised LearningResponse
Predictor
Learner
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Person 1
Person 2
ActiveActive LearningLearningResponse
Predictor
Learner
b 0 5 b ?
b 1 3 a ?
a 1 1 a ?
b 1 1 a ?
a 0 3 a ?
User is able to PURCHASE labels, at some cost… for which instances??
?
+
?
+
--
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Person 1
Person 2
BudgetedBudgeted LearningLearningResponse
Predictor
Learner
? ? ? ? 1
? ? ? ? 0
? ? ? ? 0
? ? ? ? 0
? ? ? ? 1
User is able to PURCHASE values of features, at some cost … but which features for which instances??
1 5 + t
0 5 + f
? ? ? ?
? ? ? ?
? ? ? ?
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Person 1
Person 2
BudgetedBudgeted LearningLearningResponse
Predictor
Learner
? ? ? ? 1
? ? ? ? 0
? ? ? ? 0
? ? ? ? 0
? ? ? ? 1
Significantly different from ACTIVE learning:correlations between feature values
? 5 ? ?
? ? + ?
0 ? + ?
? 9 -- ?
? ? ? ?
User is able to PURCHASE values of features, at some cost … but which features for which instances??
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n=10, Beta(10,1)
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 5 10 15 20 25 30 35 40 45
time
round-robin
random
greedy
allocational
lookahead
biased-robin
10 tests ($1/test)
Budget =$40 Beta(10,1)
# features purchased
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Budgeted Learning… so farBudgeted Learning… so farDefined framework
Ability to purchase individual feature valuesFixed LEARNING / CLASSIFICATION Budget
Theoretical resultsNP-hard in general Standard algorithms not even Approx !
Empirical Results show …Avoid Round RobinTry clever algorithms
Biased Robin Randomized Single Feature Lookahead
[Lizotte,Madani,Greiner: UAI’03], [Madani,Lizotte,Greiner: UAI’04], [Kapoor,Greiner: ECML’05]
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Person 1
Person 2
Future Work #1Future Work #1
Response
Classifier
Learner
? ? ? ? 1
b ? ? ? 0
? ? ? ? 0
? ? ? ? 0
? ? ? ? 1
?
?
?
?
?
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Future Work #2Future Work #2
Sample complexity of Budgeted LearningHow many (Ij, Xi) “probes” required to PAC-learn ?
Develop policies with guarantees on learning performance
Complex cost model … Bundling tests, … Allow learner to perform more powerful probes
purchase X3 in instance where X7 = 0 & Y = 1
More complex classifiers ?
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Person 1
Person 2
Future Work #3Future Work #3
Response
? ? ? ? ?
? ? ? ? ?
? ? ? ? ?
? ? ? ? ?
? ? ? ? ?
Goal: Find * = argmax P(D)
?
?
?
?
?
Learning Generative Model
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Projects and StatusProjects and StatusStructured Prediction
(ongoing)
Dimensional Reduction: (ongoing; RoBiC: Poster#8)
Budgeted Learning(ongoing)
SemiSupervised Learning(ongoing)
Active Learning(ongoing)
CostCurves(complete; Post#26)
LabelsMTest MTrain
0 1 .. 1
1 1 .. 0
1 0 … 1
1 1 … 0
0 0 … 1
1 1 … 0
Learner
Classifier
+
–
–
+
–
+
–
FindBiClusters
BiClusterMembershi
p
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Using Variance Using Variance Estimates to Combine Estimates to Combine Bayesian ClassifiersBayesian Classifiers
Technical DetailsTechnical Details
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MotivationMotivation
Spse many different classifiers …For each instance, want each classifier to…
“know what it knows” …… and shout LOUDEST when it knows best…
“Loudness” 1/ Variance !
C2++ +
+
+
+o
o
oo o
o
+
++ +
++
+
oo
oo o
o
+
o
++ +
+
+
+o
o
oo o
o
+ +
+ +
+
+
+o
o
oo o
o
+
C1
C3 C4
*
§
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Mixture Using VarianceMixture Using Variance
Given belief net classifierfixed (correct) structureparameters estimated from (random) datasample
Response to query “P(+c| -e, +w)” is… asymptotically normal with …(asymptotic) variance
Variance easy to compute …for simple structures (Naïve Bayes, TAN) … and for complete queries
|
2
2 2| | | |
θ |
1' ( ) ' ( )
1 nC
Q d d d dd D d DD
q q
f
f f f ff
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Experiment #4b:Experiment #4b:MUVMUV(kNB(kNB, , AdaboostAdaboost, js), js) vs vs AdaBoost(NB)AdaBoost(NB)
MUV significantly out-performs AdaBoost even when using base-classifiers that AdaBoost generated!
MUV(kNB, AdaBoost, js) better than AdaBoost[NB]
with p < 0.023
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MUV ResultsMUV ResultsSound statistical foundationVery effective classifier …
…across many real datasetsMUV(NB) better than AdaBoost(NB)!
C. Lee, S. Wang and R. Greiner; ICML’06
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Mixture Using Variance … next Mixture Using Variance … next steps?steps?Other structures (beyond NB, TAN)
Beyond just tabular CPtables for discrete variablesNoisy-orGaussians
Learn different base-classifiers from different subset of features
Scaling up to many MANY featuresoverfitting characteristics?
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Confidence in ClassifierConfidence in Classifier
Confidence of Prediction?Fit each j, j
2 to Beta(aji, bj)Compute area CDFBeta(aj, bj)(0.5)
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Temp. BP.Sore Throa
t… Colour diseaseX
35 95 Y … Pale No
22 110 N … Clear Yes
: : :
10 87 N … Pale
Semi-Supervised LearningSemi-Supervised Learning
TempPress.
Sore-Throat
… Color
32 90 N … PaleClassifier
diseaseX
No
Learner
Temp. BP.Sore Throa
t… Colour diseaseX
35 95 Y … Pale No
22 110 N … Clear Yes
10 87 N … Pale Yes
17 82 Y … Red No
33 82 N … Blue No
: : : :
4 87 N … Pale No
Labeled Training Data
UnLabeled Training Data
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ApproachesApproaches
Ignore the unlabeled dataGreat if have LOTS of labeled data
Use the unlabeled data, as is…“Semi-Supervised Learning”… based on
large margin (SVM) graph probabilistic model
Pay to get labels for SOME unlabeled data“Active Learning”
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Semi-supervised Multi-class SVMSemi-supervised Multi-class SVM
Approach: find a labeling that would yield an optimal SVM classifier, on the resulting training data.
Hard, but semi-definite relaxations can approximate
this objective surprisingly welltraining procedures are computationally
intensive, but produce high quality generalization results.
L. Xu, J. Neufeld, B. Larson, D. Schuurmans. Maximum margin clustering. NIPS-04.
L. Xu and D. Schuurmans. Unsupervised and semi-supervised multi-class SVMs. AAAI-05.
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Probabilistic Approach to Probabilistic Approach to Semi-Supervised LearningSemi-Supervised Learning
Probabilistic model: P(y|x)Context: non-IID data
Language modellingSegmenting Brain Tumor from
MR ImagesUse Unlabeled Data as
RegularizerFuture: Other applications…
C-H. Lee, W. Shaojun, F. Jiao, D. Schuurmans and R. Greiner. Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. NIPS06.
F. Jiao, S. Wang, C. Lee, R. Greiner, and D. Schuurmans. Semi-supervised conditional random fields for improved sequence segmentation and labeling. COLING/ACL06.
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Active LearningActive Learning
Pay for label to query xi that ... maximizes conditional mutual information about unlabeled data:
How to determine yi ? Take EXPECTATION wrt Yi ?
Use OPTIMISTIC guess wrt Yi ?
( , )arg min min ( | , )ii U y u u L y
u
H Y xx
( , )arg min ( | , ) ( | , )ii U i L u u L yy
u
P y H Y x x x
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Optimistic Active Learning Optimistic Active Learning using Mutual Informationusing Mutual Information
Need Optimism Need “on-line adjustment” Better than just MostUncertain, …
pima breast
Y. Guo and R. Greiner. Optimistic active learning using mutual information. IJCAI’07
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Future Work on Active Future Work on Active LearningLearningUnderstand WHY “optimism” works…
+ other applications of optimismExtend framework to deal with
non-iid datadifferent qualities of labelers…