lorenzo rosasco universit a di genova+ mit + iit lcsl.mit
TRANSCRIPT
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A consistent algorithmic framework for structured machine learning
Lorenzo RosascoUniversita di Genova+ MIT + IIT
lcsl.mit.edu
April 29th, 2019 -IPAM
joint work with C. Ciliberto (Imperial College), A. Rudi (INRIA-Paris)
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Classic supervised learning
given {(x1, y1), . . . , (xn, yn)} find f(xnew) ∼ ynew
Regression Binary classification
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Structured learning
‘‘A domain of machine learning, in which the prediction must satisfy the additional constraintsfound in structured data, poses one of machine learning’s greatest challenges: learningfunctional dependencies between arbitrary input and output domains.”
Baklr et al., Predicting structured data. MIT press, 2007. [1]
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Structured learning applications
I Image segmentation [2],
I captioning [3],
I speech recognition [4, 5],
I protein folding [6],
I ordinal regression [7],
I ranking [8].
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Examples of “structured” outputs
I Finite discrete alphabets (binary/multi-category classification, multilabel),
I strings,
I ordered lists,
I sequences.
Classically only discrete possibly output spaces.
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Classical approaches
Likelihood estimation models
I General approaches (Struct-SVM [9], Conditional Random Fields [10]),
I but limited guarantees (generalization bounds).
Surrogate approaches
I Strong theoretical guarantees,
I but ad hoc, e.g. classification [11], multiclass [12], ranking [8]. . .
We will try to take the best of both!
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Outline
Framework
Algorithms
Theory
Experiments
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Statistical learning
I (X × Y, ρ) probability space, such that ρ(x, y) = ρX (x)ρ(y|x).
I ∆ : Y × Y → [0,∞)
Problem Solve
minf∈YX
∫dρ(x, y)∆(f(x), y)
given (xi, yi)ni=1 i.i.d. samples of ρ.
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Statistical learning
I (X × Y, ρ) probability space, such that ρ(x, y) = ρX (x)ρ(y|x).
I ∆ : Y × Y → [0,∞)
Problem Solve
minf∈YX
∫dρ(x, y)∆(f(x), y)
given (xi, yi)ni=1 i.i.d. samples of ρ.
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Empirical risk minimization (ERM)
minf∈H
1
n
n∑i=1
∆(f(xi), yi)
I Statistically sound
supf∈F
∣∣∣∣∣ 1nn∑i=1
∆(f(xi), yi)−∫dρ(x, y)∆(f(x), y)
∣∣∣∣∣I Impractical: how to pick F ⊂ YX if Y is not linear?
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Inner risk
Lemma (Ciliberto, Rudi, R. ’17)
Let
f∗ = argminf∈YX
∫dρ(x, y)∆(f(x), y)
then
f∗(x) = argminy∈Y
∫dρ(y|x)∆(y, y′).
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Structured Encoding Loss Function (SELF)
Definition (SELF)
The loss function ∆ : Y × Y → [0,∞) is such that there exists
I a real separable Hilbert space (H, 〈·, ·〉) and
I maps Ψ,Φ : Y → Hsuch that ∀y, y′ ∈ Y
∆(y, y′) = 〈Ψ(y),Φ(y′)〉
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Examples of SELF
I In any finite output spaces |Y| = T
∆(y, y′) = e>y V ey′ , V ∈ RT×T .
I Symmetric positive definite loss functions, Kernel Dependency Estimator [16].
I Smooth loss functions with Y = [0, 1]d.
I Restriction of SELF are SELF, and SELF can be composed.
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Structured statistical learning
(Y,∆)
I The output space might not be a linear space and can be continuous.
I Structure encoded by the loss function.
Beyond finite, discrete spaces to include continuous output spaces, e.g.
I Manifold regression [14],
I prediction of probability distributions [15].
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Inner SELF (risk)
∫dρ(y|x)∆(f(x), y) =
∫dρ(y|x) 〈Ψ(y),Φ(y′)〉 =
⟨∫dρ(y|x)Ψ(y)︸ ︷︷ ︸
g∗(x)
,Φ(y′)
⟩
Lemma (Ciliberto, Rudi, R. ’17)
f∗(x) = argminy∈Y
〈g∗(x),Φ(y)〉
g∗ =
∫dρ(y|·)Ψ(y) = argmin
g∈HX
∫dρ(x, y)‖g(x)−Ψ(y)‖2
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Inner SELF (risk)
∫dρ(y|x)∆(f(x), y) =
∫dρ(y|x) 〈Ψ(y),Φ(y′)〉 =
⟨∫dρ(y|x)Ψ(y)︸ ︷︷ ︸
g∗(x)
,Φ(y′)
⟩
Lemma (Ciliberto, Rudi, R. ’17)
f∗(x) = argminy∈Y
〈g∗(x),Φ(y)〉
g∗ =
∫dρ(y|·)Ψ(y) = argmin
g∈HX
∫dρ(x, y)‖g(x)−Ψ(y)‖2
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Inner risk minimization (IRM)
f(x) = argminy∈Y
〈g(x),Φ(y)〉
g = argming∈G⊂HX
1
n
n∑i=1
‖g(xi)−Ψ(yi)‖2
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IRM: a general surrogate approach
I encode Ψ : Y → HI learn (xi,Ψ(yi))
ni=1 7→ g
I decode Ψ∗ : H → YΨ∗(h) = argmin
y∈Y〈h,Φ(y)〉, h ∈ H.
Xt y
hitI
It
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Some questions
I A minimization over Y instead of YX : what we gained?
I Does a SELF exist?
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Outline
Framework
Algorithms
Theory
Experiments
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Solving IRM with linear estimators
f(x) = argminy∈Y
〈g(x),Φ(y)〉, g = argming∈G
1
n
n∑i=1
‖g(xi)−Ψ(yi)‖2.
Lemma (Ciliberto, Rudi, R. ’17)
If g(x) = Wx, then
W = (X>X)−1X>Y , X ∈ Rnd, Y ∈ Hn
and
g(x) =n∑i=1
αi(x)Ψ(yi), α(x) = (XX>)−1Xx ∈ Rn
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Solving IRM with linear estimators
f(x) = argminy∈Y
〈g(x),Φ(y)〉, g = argming∈G
1
n
n∑i=1
‖g(xi)−Ψ(yi)‖2.
Lemma (Ciliberto, Rudi, R. ’17)
If g(x) = Wx, then
W = (X>X)−1X>Y , X ∈ Rnd, Y ∈ Hn
and
g(x) =
n∑i=1
αi(x)Ψ(yi), α(x) = (XX>)−1Xx ∈ Rn
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Implicit IRM for linear estimators
f(x) = argminy∈Y
〈g(x),Φ(y)〉, g = argming∈G
1
n
n∑i=1
‖g(xi)−Ψ(yi)‖2.
Lemma (Ciliberto, Rudi, R. ’17)
If
g(x) =
n∑i=1
αi(x)Ψ(yi),
then
f(x) = argminy∈Y
n∑i=1
αi(x)∆(yi, y)
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Other linear estimators
g(x) =
n∑i=1
αi(x)Ψ(yi),
I Kernel methods g(x) = Wγ(x), where γ : X → (HΓ, 〈·, ·〉Γ).
I Local kernel estimators.
I Spectral filters.
I Sketching/random features/Nystrom.
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Computations: no free lunch
Training
g = argming∈G
1
n
n∑i=1
‖g(xi)−Ψ(yi)‖2.
Computing (αi(x))i depends only on the inputs and is efficient.
Prediction
f(x) = argminy∈Y
n∑i=1
αi(x)∆(yi, y).
Requires problem specific decoding and can be hard.
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Outline
Framework
Algorithms
Theory
Experiments
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Consistency and excess risk bounds
Problem Solve
minf∈YX
R(f), R(f) =
∫dρ(x, y)∆(f(x), y)
given (xi, yi)ni=1 i.i.d. samples of ρ.
Excess risk Convergence and rates on
R(f)−R(f∗)
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A relaxation error analysis
Let
L(g) =
∫dρ(x, y)‖g(x)−Ψ(y)‖2
Theorem (Ciliberto, Rudi, R. ’17)
The following hold:
I Fisher consistencyf∗(x) = Ψ∗g∗(x). a.s.
I Comparison inequality, for all g and f(x) = Ψ∗g(x) a.s.
R(f)−R(f∗) ≤ c∆√L(g)− L(g∗)
wherec∆ = sup
y∈Y‖Ψ(y)‖
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Consistency and rates for IRM-KRR
Let gλ(x) = Wλγ(x) with
Wλ = argminW∈L2(HΓ,H)
1
n
n∑i=1
‖Wxi −Ψ(yi)‖2 + λ‖W‖22.
Theorem (Ciliberto, Rudi, R. ’17)
Let κγ = supx∈X ‖γ(x)‖. Assume ∃W∗ ∈ L2(HΓ,H) such that g∗(x) = W∗x. Ifλn = O(1/
√n), then with probability at least 1− 8e−τ√
L(g)− L(g∗) ≤ 24 κγ (1 + ‖W‖2) τ2n−1/4.
and for f(x) = Ψ∗gλ(x) a.s.
R(f)−R(f∗) ≤ 24 κγ c∆(1 + ‖W‖2) τ2n−1/4.
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Remarks
I This is the first result establishing consistency and rates for structured prediction, see [13]for similar efforts.
I The bound on L(g)− L(g∗) extend results in [17] under weaker assumptions.
I The constant c∆ is problem dependent. Finding a general estimate is an open problem[18].
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Outline
Framework
Algorithms
Theory
Experiments
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Ranking
Rank Loss
Linear [8] 0.430 ± 0.004Hinge [19] 0.432 ± 0.008
Logistic [20] 0.432 ± 0.012SVM Struct [9] 0.451 ± 0.008
IRM-KRR 0.396± 0.003
Ranking movies in the MovieLens dataset [21] (ratings (from 1 to 5) of 1682 movies by 943users). The goal is predict preferences of a given user, i.e. an ordering of the 1682 movies,according to the user’s partial ratings. We the loss [8]
∆rank(y, y′) =1
2
M∑i,j=1
γ(y′)ij (1− sign(yi − yj)),
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Fingerprints reconstruction
∆ Deg.
KRLS 26.9 ± 5.4MR[14] 22 ± 6SP (ours) 18.8± 3.9
Average absolute error (in degrees) for the manifold structured estimator (SP), the manifoldregression (MR) approach in [14] and the KRLS baseline. (Right) Fingerprint reconstruction ofa single image where the structured predictor achieves 15.7 of average error while KRLS 25.3.The loss is the geodesic on S
∆S(z, y) = arccos (〈z, y〉)2
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Summing up
I First consistent algorithmic framework for StructML.
I A general surrogate approach.
I TBD: decoding computations+ beyond linear estimators.
Openings
Multiple openings for post-docs/PhD positions!
→ Launching: Machine Learning Genova Center!@lrntzrsc
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Related papers
I Ciliberto, Rudi and Rosasco A consistent regularization approach for structured prediction.NIPS 2016.
I Ciliberto, Rudi and Rosasco, and Pontil. Consistent multitask learning with nonlinearoutput relations, NIPS 2017.
I Rudi, Ciliberto, Marconi, and Rosasco. Manifold structured prediction. NIPS 2018.
I Mroueh, Poggio, Rosasco, and Slotine. Multiclass learning with simplex coding. NIPS2012.
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