Post on 26-Dec-2015
Model Selection via Bilevel Optimization
Kristin P. Bennett, Jing Hu, Xiaoyun Ji, Gautam Kunapuli and Jong-Shi Pang
Department of Mathematical SciencesRensselaer Polytechnic InstituteTroy, NY
Convex Machine LearningConvex optimization approaches to machine
learning has been major obsession of machine learning for last ten years.
But are the problems really convex?
Outline The myth of convex machine learning Bilevel Programming Model Selection
Regression Classification
Extensions to other machine learning tasks Discussion
Modeler’s ChoicesData
Function
Loss/Regularization
1 1
2 1
1
y
y
y
x
x
x
2
1' 22
min max(| | ,0) || ||i ii
C y x w w
Optimization Algorithm
ˆ( )f x x w
CONVEX!
w( )f x x w
Many Hidden Choices Data:
Variable Selection Scaling Feature Construction Missing Data Outlier removal
Function Family: linear, kernel (introduces kernel parameters)
Optimization model loss function regularization Parameters/Constraints
Data
Function
Loss/Regularization
Cross-Validation Strategy
Generalization Error
, , 1t ttraitest tn T
[ ], ,[ ] Xy yX
1
1 1| |
t
Tt
i it itT
x w y
NONCONVEX
Cross-Validation:
C,ε, [X,y]
Optimization Algorithm
w
1 22
min max(| | ,0) || ||i ii
C y x w w
ˆ( )f x x w
ˆ( )f x x w
How does modeler make choices? Best training set error Experience/policy Estimate of generalization error
Cross-validation Bounds
Optimize generalization error estimate Fiddle around. Grid Search Gradient methods Bilevel Programming
Splitting Data for T-fold CV
-th partition
t
disjoint partitions of
T
validation set for -th fold
t
t
\ training set for -th fold
t t
t
CV via Grid SearchFor every C, ε • For every validation set, Solve model on corresp.
training set, and to estimate loss for
•Estimate generalization error for C, ε
Return best values for C,εMake final model using C,ε
C
ε
,C
t
tt
Bilevel Program for T folds
Prior Approaches: Golub et al., 1979, Generalized Cross-Validation for one parameter in Ridge Regression
CV as Continuous Optimization Problem
,1
2
2
1min
1 s.t. arg min max ,0
2
1,..,
t
t
Tt
i iC
t it
tj j
j
yT
C y
t T
w
x w
w x w wT inner-level training problems
Outer-level validation problem
Benefit: More Design Variables
Add feature box constraint: in the inner-level problems.
w w w
, ,1
2
2
1min
1 s.t. arg min max ,0
2
t
t
Tt
i iCt it
tj j
j
yT
C y
w
w w w
x w
w x w w
-insensitive Loss Function max ,0z
Inner-level Problem for t-th Fold 2
2
2
2,
1min max , 0
2
1 min
2
s.t.
t
t
t t
t
t tj j
j
t tj
j
t tj j j
t tj j j
C y
C
y
y
w w w
w ξ
x w w
w
x w
x w
0tj
t
w w w
Optimality (KKT) conditions for fixed , ,C w
, t
, t
, ,
, t
, t
t , , , ,
j
0 + + 0
0 + + 0
0 0
0 0
0 0
0 =t
t tj j j jt t
j j j j tt t tj j j
t
t
t t t tj j j
y
y j
C
x w
x w
γ w w
γ w w
w x γ γ
where 0 a b a b
Key Transformation KKT for the inner level training problems
are necessary and sufficient Replace lower level problems by their
KKT Conditions Problem becomes an Mathematical
Programming Problem with Equilibrium Constraints (MPEC)
Bilevel Problem as MPEC
, , , 1
, t
, t
, ,
, t
, t
1min
s.t. for 1,...,
0 + + 0
0 + + 0
0 0
0 0
0 0
0 =
t
t
Tt
i iC t it
t tj j j jt t
j j j j tt t tj j j
t
t
yT
t T
y
y j
C
w w
x w
x w
x w
γ w w
γ w w
w t , , , ,
j t
t t t tj j j
x γ γ
Replace T inner-level problems with
corresponding optimality conditions
MPEC to NLP via Inexact Cross ValidationRelax “hard” equilibrium constraints
to “soft” inexact constraints
tol is some user-defined tolerance.
tol tol
, 00 0
a ba b
a b
, 00 0
0
a ba b
a b
Solvers Strategy: Proof of concept using nonlinear general
purpose solvers from NEOS on NLP FILTER, SNOPT Sequential Quadratic Programming Methods FILTER results almost always better.Many possible alternatives: Integer Programming Branch and Bound Lagrangian Relaxations
Computational Experiments: DATASynthetic (5,10,15)-D Data with Gaussian and Laplacian
noise and (3,7,10) relevant features. NLP: 3-fold CV Results: 30 to 90 train, 1000 test points, 10 trialsQSAR/Drug Design 4 datasets, 600+ dimensions reduced to 25 top
principal components. NLP: 5-fold CV Results: 40 – 100 train, rest test, 20 trials
Cross-validation Methods Compared Unconstrained Grid: Try 3 values each for C,ε Constrained Grid: Try 3 values each for C, ε, and {0, 1} for each component of Bilevel/FILTER: Nonlinear program
solved using off-the-shelf SQP algorithm via NEOS
w
15-D Data: Objective Value
0
0.5
1
1.5
2
2.5
3
15 pts 30 pts 60 pts 90 pts
Unc GridCon GridFilter
15-D Data: Computational Time
0
100
200
300
400
500
600
700
800
900
15 pts 30 pts 60 pts 90 pts
Unc GridCon GridFilter
15-D Data: TEST MAD
0
0.5
1
1.5
2
2.5
3
15 pts 30 pts 60 pts 90 pts
Unc GridCon GridFilter
QSAR Data: Objective Value
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Aquasol BBB Cancer CCK
Unc GridCon GridFilter
QSAR Data: Computation Time
0
200
400
600
800
1000
1200
1400
Aquasol BBB Cancer CCK
Unc GridCon GridFilter
QSAR Data: TEST MAD
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Aquasol BBB Cancer CCK
Unc GridCon GridFilter
Classification Cross ValidationGiven sample data from
two classes.
Find classification function that minimizes out-of-sample estimate of classification error
( , ) 1,...,i iy i x
1
-1
Lower level - SVM
' 1x w
Define parallel planes Minimize points on
wrong side Maximize margin of
separation
' 1x w
2
|| ||w
Lower Level Loss Function: Hinge Loss
max 1 ,0z
Measures distance of points that violate the appropriate hyperplane constraints,ib y w x
Lower Level Problem: SVC with box
2
2, ,min
2
s.t. 1
0
t tt
t
t tj
b j
t tj j t j
tj
t
y b
w ξ
w
w
w
x w
w
2
2
min max 1 , 02
tt
t
t tj j t
jb
y b
w w w
w x wR
Inner-level KKT Conditions
t
,
,
, ,
0 1 0
0 1 0
0 0
0 0
0 =
0
t
t
t tj j j t j
tt tj j
t t
t t
t t t tj j j
j
tj j
j
y bj
y
y
x w
γ w w
γ w w
w x γ γ
Outer-level Loss Functions Misclassification Minimization Loss (MM)
Loss function used in classical CV Loss = 1, if validation pt misclassified,
0, otherwise(computed using step function, )
Hinge Loss (HL) Both inner and outer levels use same loss
function Loss = distance from
(computed using max function, ) ib y w x
*
max ,0
Hinge Loss is Convex Approx. of Misclassification Minimization Loss
max 1 ,0z
*z
Hinge Loss Bilevel Program (BilevelHL)
Replace max in outer level objective with convex constraints
Replace inner-level problems with KKT conditions
,
1
2
2
1min
s.t. , arg min2
max 1 ( ),0
max 1 ( ),0
t
t
T
t it
tt
jb
ti i t
j j
T
b
y b
y b
w
w w ww w
x w
x wR
,
,
min
min s.t. 1 ( )
max 1 ( ,0 0
)t
t
tt
ti
bt ti i i
b ti i
i
tt ty b
z
z y bz
w
wxx ww
Hinge Loss MPEC , , , 1
t
1min
s.t. 0, 0,
and for 1,...,
1 ( )
0
0
tt
t
Tti
b t it
t ti i i t
tti
tj j j
zT
t T
z y bi
z
y
w w
w
x w
x w
,
,
, ,
1 0
0 1 0
0 0
0 0
0 =
0
t
t
tt j
tt tj j
t t
t t
t t t tj j j
j
tj j
j
bj
y
y
γ w w
γ w w
w x γ γ
Misclassification Min. Bilevel Program (BilevelMM)
,
1
2
2
1min
s.t. , arg max 1 ( )
( )
,0m n
*
i2
t
t
j
T
t it
t
b
t
j
t
t
i i
j
Ty b
bb y
w
w w w
x
www
w
xR
1, if 0,( )
* 0, if 0.n
nn
a
a
a
Misclassifications are counted using the step function, defined component
wise for a n-vector as
The Step FunctionMangasarian (1994) showed that
and that any solution, , to the LP satisfies
arg min : 0 1*
ζ
a ζ a ζ
0 0
0 1 0
ζ a z
z ζ
,z ζ
Misclassifications in the Validation Set
Validation point misclassified when the sign of is negative i.e.,
This can be recast for all validation points (within the t-th fold) as
( )ti i ty b x w
( ) 0*
t ti i i ty b x w
0 1
arg min ( )t
t
t t ti i i t
i
y b
ζ
ζ x w
Misclassification Minimization Bilevel Program (revisited)
,1
0 1
2
2
1min
s.t. arg min ( )
, arg min max 1 ( ),02
t
t
t
Tti
t it
t ti i i t
i
tt j j
jb
T
y b
b y b
w
ζ
w w w
ζ x w
w x w wR
Inner-level problems to determine misclassified
validation points
Inner-level training problems
Outer-level average misclassification minimization
Misclassification Minimization MPEC
, , , 1
t
1min
s.t. 0, 0, and for 1,...,
0 0
0 1 0
0 1 0 0 1 0
tt
t
Tti
b t it
t t ti i i t i
tt ti i
t tj j j t j
t tj j
T
t T
y b zi
z
y bj
w w
w
x w
x w
,
,
, ,
0 0
0 0 0 =
0t
t
t
t t
t t
t t t tj j j
j
tj j
j
y
y
γ w wγ w w
w x γ γ
Inexact Cross Validation NLP Both BilevelHL and BilevelMM MPECs
are transformed to NLP by relaxing equilibrium constraints (inexact CV)
Solved using FILTER on NEOS These are compared with classical cross
validation: unconstrained and constrained grid.
Experiments: Data sets
3-fold cross validation for model selection
Average results for 20 train test splits
Computational Time
0
1000
2000
3000
4000
5000
6000
7000
8000
pima cancer heart iono bright dim
Unc-GridCon-GrdBilevel-MisMinBilevel-Hinge
Training CV Error
0
5
10
15
20
25
30
35
40
45
pima cancer heart iono bright dim
Unc-GridCon-GrdBilevel-MisMinBilevel-Hinge
Testing Error
0
5
10
15
20
25
pima cancer heart iono bright dim
Unc-GridCon-GrdBilevel-MisMinBilevel-Hinge
Number of Variables
0
5
10
15
20
25
30
35
pima cancer heart iono bright dim
Unc-GridCon-GrdBilevel-MisMinBilevel-Hinge
Progress Cross Validation is a bilevel problem
solvable by continuous optimization methods
Off-the-shelf NLP algorithm – FILTER solved classification and regression
Bilevel Optimization extendable to many Machine Learning problems
Extending Bilevel Approach to other Machine Learning Problems Kernel Classification/Regression Variable Selection/Scaling Multi-task Learning Semi-supervised Learning Generative methods
Semi-supervised Learning Have labeled data, and
unlabeled data Treat missing labels, , as design variables
in the outer level Lower level problems are still convex
1,i i iy
Ω x
1,u u
Ψ x m
uz
Semi-supervised Regression
1
, , ,
,
2
2
min
max ,0
s.t. , arg min max ,0
1
2
n
i iC Di
j jj
u ub u
b y
C b y
b D b z
zΩ
Ω
w Ψ
x w
x w
w x w
w
Outer level minimizes error on labeled data
to find optimal parameters and labels -insensitive loss on
labeled data in inner
level
-insensitive loss on
unlabeled data in inner
level
Inner level regularization
Discussion New capacity offers new possibilities: Outer level objectives? Inner level problem? classification, ranking, semi-supervised, missing values, kernel selection, variable
selection, … Need special purpose algorithms for
greater efficiency, scalability, robustnessThis work was supported by Office of Naval Research Grant N00014-06-1-0014.
Experiments: Bilevel CV Procedure Run BilevelMM/BilevelHL to compute
optimal parameters, Drop descriptors with small Create model on all training data
using Compute test error on hold-out set
ˆˆ ,ww
ˆˆ ,w
ˆˆ( , )bw
( , )
1 1 ˆˆsign2
test
testytest
ERROR b y x
w x
Experiments: Grid Search CV Procedure Unconstrained Grid: Try 6 values for on a log10 scale Constrained Grid: Try 6 values for and {0, 1} for each
component of (perform RFE if necessary) Create model on all training data using
optimal grid point Compute test error on hold-out set
w
ˆˆ( , )bwˆˆ ,w
( , )
1 1 ˆˆsign2
test
testytest
ERROR b y x
w x
Extending Bilevel Approach to other Machine Learning Problems Kernel Classification/Regression Different Regularizations (L1, elastic nets)
Enhanced Feature Selection Multi-task Learning Semi-supervised Learning Generative methods
Enhanced Feature Selection Assume at most descriptors allowed Introduce outer-level constraint
(with counting the non-zero elements of )
Rewrite constraint, observing that Get additional conditions for
maxn
0 maxnww
0
0 *w e w
1
0 00 0
n
m maxm
n
δ w dd e δ
*δ w
Kernel Bilevel Discussion Pros
Performs model selection in feature space Performs feature selection in input space
Cons Highly nonlinear model Difficult to solve
Kernel Classification (MPEC form)
, , , 1
1min
s.t. 0, 0, and for 1,...,
0 ( , ; ) 0
0 1 0
0 ( , ; )
tt
t
t
t
Tti
b C t it
t t ti i k k i k t i
k t
t ti i
t tj j k k j k
k
TC
t T
y y b zi
z
y y
α p
p
κ x x p
κ x x p 1 0
0 0
0t
tt j
t
t tj j
tj j
j
bj
C
y
Is it okay to do 3-folds
Applying the “kernel trick” Drop the box constraint, Eliminate from the optimality conditions
Replace with an appropriate
w w ww
0 0
0 1 0
0 1 0
0 0
0
t
t
t
t t ti i k k t i
k t
t ti i
t t tj j k k t j
k t
t
i k
j
tj j
tj
k
jj
y y b zi
z
y y bj
C
y
x x
x x
kx x ( , )kκ x x
Feature Selection with Kernels Parameterize the kernel with
such that if the n-th descriptor vanishes from
Linear kernel
Polynomial kernel
Gaussian kernel
, 0n p p0np
( , ; )kκ x x p
( , ; ) diagd
k k c κ x x p x p x
( , ; ) diagk kκ x x p x p x
( , ; ) exp 'diagk k k κ x x p x x p x x
Kernel Regression (Bilevel form)
,
, ,
, , 1
, ,
,
, , , ,
1min ( , ; )
max ( , ; ) ,0
s.t. arg min1
( , ; )2
t
t t
t t
t t
Tt tk k i k i
C t i kt
t tk k j k j
j kt
t t t tk k l l l k
k l
yT
C y
α p
α
κ x x p
κ x x p
α
κ x x p