Machine Learning Theory CS 446 1. SVM risk SVM risk Consider the - - PowerPoint PPT Presentation

Machine Learning Theory

CS 446

• 1. SVM risk

SVM risk

Consider the empirical and true/population risk of SVM: given f, R(f) = E

• (Y ˆ

f(X))

• ,
• R(f) = 1

n

• i=1

(yi ˆ f(xi)), and furthermore define excess risk R(f) − R(f).

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SVM risk

Consider the empirical and true/population risk of SVM: given f, R(f) = E

• (Y ˆ

f(X))

• ,
• R(f) = 1

n

• i=1

(yi ˆ f(xi)), and furthermore define excess risk R(f) − R(f).

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SVM risk

Consider the empirical and true/population risk of SVM: given f, R(f) = E

• (Y ˆ

f(X))

• ,
• R(f) = 1

n

• i=1

(yi ˆ f(xi)), and furthermore define excess risk R(f) − R(f). What’s going on here?

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SVM risk

Consider the empirical and true/population risk of SVM: given f, R(f) = E

• (Y ˆ

f(X))

• ,
• R(f) = 1

n

• i=1

(yi ˆ f(xi)), and furthermore define excess risk R(f) − R(f). What’s going on here? (I just tricked you into caring about theory.)

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f).

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F?

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F? Answer: depends on kernel!)

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F? Answer: depends on kernel!) (Question: is ¯ f = arg minf∈F R(f)?

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F? Answer: depends on kernel!) (Question: is ¯ f = arg minf∈F R(f)? Answer: no; in general R( ¯ f) ≥ R( ˆ f)!)

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F? Answer: depends on kernel!) (Question: is ¯ f = arg minf∈F R(f)? Answer: no; in general R( ¯ f) ≥ R( ˆ f)!) Nature labels according to some g (not necessarily inside F!): R( ˆ f) = R(g) (inherent unpredictability) + R( ¯ f) − R(g) (approximation gap) + R( ¯ f) − R( ¯ f) (estimation gap) + R( ˆ f) − R( ¯ f) (optimization gap) + R( ˆ f) − R( ˆ f) (generalization gap)

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Let’s also define true/population risk minimizer ¯ f := arg minf∈F R(f). (Question: What is F? Answer: depends on kernel!) (Question: is ¯ f = arg minf∈F R(f)? Answer: no; in general R( ¯ f) ≥ R( ˆ f)!) Nature labels according to some g (not necessarily inside F!): R( ˆ f) = R(g) (inherent unpredictability) + R( ¯ f) − R(g) (approximation gap) + R( ¯ f) − R( ¯ f) (estimation gap) + R( ˆ f) − R( ¯ f) (optimization gap) + R( ˆ f) − R( ˆ f) (generalization gap) Let’s go through this step by step.

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Inherent unpredictability

Nature labels according to some g (not necessarily inside F!): R(g) (inherent unpredictability)

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Inherent unpredictability

Nature labels according to some g (not necessarily inside F!): R(g) (inherent unpredictability) If g is the function with lowest classification error, we can write down an explicit form: g(x) := sign(Pr[Y = +1|X = x] − 1/2). If g minimizes R with convex , again can write down g pointwise via Pr[Y = +1|X = x].

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Approximation gap

¯ f minimizes R over F, and g is chosen by nature; consider R( ¯ f) − R(g). (approximation gap)

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Approximation gap

¯ f minimizes R over F, and g is chosen by nature; consider R( ¯ f) − R(g). (approximation gap) We’ve shown that if R is misclassification, F is affine classifier, g is quadratic, can have gap 1/4. We can make this gap arbitrarily small if F is: 2 layer wide network, RBF kernel SVM, polynomial classifier with arbitrary degree . . . What is F for SVM?

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Approximation gap

Consider SVM with no kernel. Can we only say F :=

• x → w

Tx : w ∈ Rd

?

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Approximation gap

Consider SVM with no kernel. Can we only say F :=

• x → w

Tx : w ∈ Rd

? Note, for ˆ w := arg minw R(w) + λ

2 w2,

λ 2 ˆ w2 ≤ R( ˆ w) + λ 2 ˆ w2 ≤ R(0) + λ 2 02 = 1 n

n

• i=1
• 1 − 0

Txiyi

• + = 1,

and so SVM is working with the finer set Fλ :=

• w → w

Tx : w2 ≤ 2

λ

• .

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Approximation gap

What about kernel SVM?

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Approximation gap

What about kernel SVM? Now working with Fk :=   x →

n

• i=1

αiyik(xi, x) : α ∈ Rn    which is a random variable! ((xi, yi))n

i=1 given by data.

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Approximation gap

What about kernel SVM? Now working with Fk :=   x →

n

• i=1

αiyik(xi, x) : α ∈ Rn    which is a random variable! ((xi, yi))n

i=1 given by data.

This function class is called a reproducing kernel hilbert space (RKHS). We can use it to develop a refined notion Fk,λ.

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Approximation gap

What about kernel SVM? Now working with Fk :=   x →

n

• i=1

αiyik(xi, x) : α ∈ Rn    which is a random variable! ((xi, yi))n

i=1 given by data.

This function class is called a reproducing kernel hilbert space (RKHS). We can use it to develop a refined notion Fk,λ. Going forward: we always try to work with the tightest possible function class defined by the data and algorithm.

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Estimation gap

¯ f minimizes R over F.

• R( ¯

f) − R( ¯ f) (estimation gap)

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Estimation gap

¯ f minimizes R over F.

• R( ¯

f) − R( ¯ f) (estimation gap) If ((xi, yi))n

i=1 drawn IID from same distribution as E in R,

by central limit theorem, R( ¯ f) − − − − →

n→∞ R( ¯

f). Next week, we’ll discuss high probability bounds for finite n.

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Optimization gap

ˆ f ∈ F minimizes R, and ¯ f ∈ F minimizes R.

• R( ˆ

f) − R( ¯ f) (optimization gap)

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Optimization gap

ˆ f ∈ F minimizes R, and ¯ f ∈ F minimizes R.

• R( ˆ

f) − R( ¯ f) (optimization gap) This is algorithmic: we reduce this number by optimizing better. We’ve advocated the use of gradient descent. Many of these problems are NP-hard even in trivial cases. (Linear separator with noise and 3-node neural net are NP-hard.) If R uses a convex loss and ˆ f has at least one training mistake, relating R and test set mislcassifications can be painful.

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Optimization gap

ˆ f ∈ F minimizes R, and ¯ f ∈ F minimizes R.

• R( ˆ

f) − R( ¯ f) (optimization gap) This is algorithmic: we reduce this number by optimizing better. We’ve advocated the use of gradient descent. Many of these problems are NP-hard even in trivial cases. (Linear separator with noise and 3-node neural net are NP-hard.) If R uses a convex loss and ˆ f has at least one training mistake, relating R and test set mislcassifications can be painful. Specifically considering SVM. This is a convex optimization problem. We can solve it in many ways (primal, dual, projected gradient descent, coordinate descent, Newton, et.), it doesn’t really matter so long as we end up close; the primal solutions are unique.

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Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit.

9 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. Before, we said “By CLT, R( ¯ f) − − − − →

n→∞ R( ¯

f)”. Is this quantity the same?

9 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. Before, we said “By CLT, R( ¯ f) − − − − →

n→∞ R( ¯

f)”. Is this quantity the same? No! ˆ f is a random variable!

9 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. Before, we said “By CLT, R( ¯ f) − − − − →

n→∞ R( ¯

f)”. Is this quantity the same? No! ˆ f is a random variable! Controlling this quantity will be the main topic next week!

9 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. Before, we said “By CLT, R( ¯ f) − − − − →

n→∞ R( ¯

f)”. Is this quantity the same? No! ˆ f is a random variable! Controlling this quantity will be the main topic next week! Basic gist: this quantity degrades the “larger” F is. Measuring “size” of F is tricky business! We can get something pretty nice by considering Fk,λ (kernel and regularizer).

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Decomposing excess risk, revisited

Nature labels according to some g (not necessarily inside F!); ˆ f ∈ F is ERM, ¯ f ∈ F is best for R. R( ˆ f) = R(g) (inherent unpredictability) + R( ¯ f) − R(g) (approximation gap) + R( ¯ f) − R( ¯ f) (estimation gap) + R( ˆ f) − R( ¯ f) (optimization gap) + R( ˆ f) − R( ˆ f) (generalization gap)

10 / 22

Decomposing excess risk, revisited

Nature labels according to some g (not necessarily inside F!); ˆ f ∈ F is ERM, ¯ f ∈ F is best for R. R( ˆ f) = R(g) (inherent unpredictability) + R( ¯ f) − R(g) (approximation gap) + R( ¯ f) − R( ¯ f) (estimation gap) + R( ˆ f) − R( ¯ f) (optimization gap) + R( ˆ f) − R( ˆ f) (generalization gap) Key point: reason about tightest F defined by data and algorithm. For SVM, this means Fk,λ, a random variable depending on the data, regularization, and kernel. Smaller F worsens approximation, but improves generalization (and might improve optimization).

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SVM risk, revisited

• 11 / 22

• 2. k-nn

Consider k-nn with varying k.

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Consider k-nn with varying k.

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Consider k-nn with varying k. We can also interpret this in terms of function class size! 1-nn breaks Rd into n data-dependent regions; k-nn breaks Rd into potentially nk data-dependent regions!

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• 3. Deep networks

Do things still work?

Some parts of the story seem off

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Do things still work?

Some parts of the story seem off

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Do things still work?

Some parts of the story seem off

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Do things still work?

Some parts of the story seem off E.g., fiddling with regularization isn’t the way to control excess risk.

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Conflated concerns

For SVM, we could decouple the components; e.g., the optimization problem is convex with a unique optimum, any reasonable solver suffices.

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Conflated concerns

For SVM, we could decouple the components; e.g., the optimization problem is convex with a unique optimum, any reasonable solver suffices. In deep networks, the choice of solver affects all other aspects of the problem, and moreover the solver is affected by the data and architecture/approximation.

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• 4. Summary

Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Now it depends highly on the algorithm, not just the data! Let’s also pick true/population risk minimizer ¯ f := arg minf∈F R(f); this choice is now more careful as well.

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Decomposing excess risk

ERM ˆ f is an approximate ERM: R( ˆ f) ≈ minf∈F R(f). Now it depends highly on the algorithm, not just the data! Let’s also pick true/population risk minimizer ¯ f := arg minf∈F R(f); this choice is now more careful as well. Nature labels according to some g (not necessarily inside F!): R( ˆ f) = R(g) (inherent unpredictability) + R( ¯ f) − R(g) (approximation gap) + R( ¯ f) − R( ¯ f) (estimation gap) + R( ˆ f) − R( ¯ f) (optimization gap) + R( ˆ f) − R( ˆ f) (generalization gap)

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Approximation gap

¯ f minimizes R over F, and g is chosen by nature; consider R( ¯ f) − R(g). (approximation gap)

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Approximation gap

¯ f minimizes R over F, and g is chosen by nature; consider R( ¯ f) − R(g). (approximation gap) We know that there exist wide shallow networks that make this arbitrarily small. But what can we say about the networks (and parameters) used in practice?

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Approximation gap

¯ f minimizes R over F, and g is chosen by nature; consider R( ¯ f) − R(g). (approximation gap) We know that there exist wide shallow networks that make this arbitrarily small. But what can we say about the networks (and parameters) used in practice? We must consider the optimization and the data when constructing this class. It will be more complicated than Fk,λ with kernel SVM. . .

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Optimization gap

ˆ f ∈ F minimizes R, and ¯ f ∈ F minimizes R.

• R( ˆ

f) − R( ˆ f) (optimization gap)

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Optimization gap

ˆ f ∈ F minimizes R, and ¯ f ∈ F minimizes R.

• R( ˆ

f) − R( ˆ f) (optimization gap) This problem is NP-hard in general, but we can find global optima with gradient descent and other solvers. How do we know? We get 0 error. The solutions are no longer unique, and depend on the optimization algorithm. We pick an optimizer with an eye towards test error! Network architecture choices and data seem to heavily influence

• ptimization.

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Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit.

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Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit.

18 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. It seems all reasonable deep networks will get 0 training error on many tasks (certainly true in computer vision?).

18 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. It seems all reasonable deep networks will get 0 training error on many tasks (certainly true in computer vision?). The generalization question is currently a mess, and we have no idea how to measure “size”.

18 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. It seems all reasonable deep networks will get 0 training error on many tasks (certainly true in computer vision?). The generalization question is currently a mess, and we have no idea how to measure “size”. E.g., sometimes increasing number nodes or edges can decrease excess risk.

18 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. It seems all reasonable deep networks will get 0 training error on many tasks (certainly true in computer vision?). The generalization question is currently a mess, and we have no idea how to measure “size”. E.g., sometimes increasing number nodes or edges can decrease excess risk. Convolutions and other magical choices like batch norm might be helping in ways no one understands.

18 / 22

Generalization

ˆ f is returned by ERM. R( ˆ f) − R( ˆ f) This quantity is excess risk; when it is small, we say we generalize, otherwise we overfit. It seems all reasonable deep networks will get 0 training error on many tasks (certainly true in computer vision?). The generalization question is currently a mess, and we have no idea how to measure “size”. E.g., sometimes increasing number nodes or edges can decrease excess risk. Convolutions and other magical choices like batch norm might be helping in ways no one understands. “Normal” regularization (e.g., weight decay) can hurt other aspects (e.g.,

• ptimization).

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corrected from lecture

Test error?

Is test error all we should discuss and analyze?

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Test error?

Is test error all we should discuss and analyze? Adversarial robustness; consider self-driving cars! Theory questions: certified robustness, algorithmic defenses, . . .

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Test error?

Is test error all we should discuss and analyze? Adversarial robustness; consider self-driving cars! Theory questions: certified robustness, algorithmic defenses, . . . Deep reinforcement learning; many aspects are very finicky.

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Test error?

Is test error all we should discuss and analyze? Adversarial robustness; consider self-driving cars! Theory questions: certified robustness, algorithmic defenses, . . . Deep reinforcement learning; many aspects are very finicky. Architecture search and tradeoffs.

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Test error?

Is test error all we should discuss and analyze? Adversarial robustness; consider self-driving cars! Theory questions: certified robustness, algorithmic defenses, . . . Deep reinforcement learning; many aspects are very finicky. Architecture search and tradeoffs. . . .

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• 5. Closing comments on theory

ML theory vs CS theory

CS Theory. Design and analysis of algorithms. Time complexity, space complexity, . . . Often worst-case.

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ML theory vs CS theory

CS Theory. Design and analysis of algorithms. Time complexity, space complexity, . . . Often worst-case. ML Theory. Design and analysis of ML algorithms. Time complexity, space complexity, . . . sample complexity, label complexity, covariate shift . . . Often average-case.

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Why?

Some old methods like SVM and AdaBoost were rooted in theory from their earliest genesis. Now, the pendulum has swung (and broken off?) in applied work; it has been a while since theory contributed a genuine algorithm, although theory sometimes guides practice.

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Why?

Some old methods like SVM and AdaBoost were rooted in theory from their earliest genesis. Now, the pendulum has swung (and broken off?) in applied work; it has been a while since theory contributed a genuine algorithm, although theory sometimes guides practice. Why do theory?

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Why?

Some old methods like SVM and AdaBoost were rooted in theory from their earliest genesis. Now, the pendulum has swung (and broken off?) in applied work; it has been a while since theory contributed a genuine algorithm, although theory sometimes guides practice. Why do theory? For some problems in DL, it might help. (E.g., adversarial robustness.)

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Why?

Some old methods like SVM and AdaBoost were rooted in theory from their earliest genesis. Now, the pendulum has swung (and broken off?) in applied work; it has been a while since theory contributed a genuine algorithm, although theory sometimes guides practice. Why do theory? For some problems in DL, it might help. (E.g., adversarial robustness.) Personally, I do it because I want to (curiosity/fun/etc).

21 / 22

Summary

Decomposition of risk into estimation, approximation, generalization,

• ptimization.

Decoupling of concerns for some problems (SVM) versus tangled concerns for others (DL). General thought process: (a) we carefully identify the tightest class of predictors F considered by the algorithm on this particular data, (b) generalization seems to worsen with some intricate notion of size of F. Next week: statistical learning theory, focusing on estimation and generalization.

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