tutorial.ppt

Machine Learning
Avrim Blum
Carnegie Mellon University
Machine Learning:
a 1-semester course in 2 hrs
(no tipping)
[FOCS 2003 tutorial]
Machine Learning:
a tour through some favorite results,
directions, and open problems
Your guide:

Philosophy of the tour
• nice/clean theory.
• relation to other TOC issues / tools
have potential use in other TOC areas.
• good open problems.
• useful in practice
• I know something about it.
A set of topics with:

Itinerary
Preparation: Basic concept learning setting
(learning from examples)
Stop 1:Batch learning:Algs&Sample complexity
(why am I trying to optimize on data I’ve seen: why should this carry
over to the future?)
Stop 2:Online learning (expert advice and other problems)
Stop 3:SQ and Fourier (strong complexity results)
Stop 4:Current “hot” practical issues.
Emphasize connections to other TOC areas/issues (incl apx
algs, online algs, auctions, complexity, crypto)

The concept learning setting
• Imagine learning algorithm trying to decide
which loan applicants are bad credit risks.
• Might represent each person by n features.
(e.g., income range, debt load, employment history, etc.)
• Take sample S of data, labeled according to
whether they were/weren’t good risks.
• Goal of algorithm is to use data seen so far
produce good prediction rule (a “hypothesis”)
h(x) for future data.

The concept learning setting
E.g.,
Given this data, some reasonable rules might
be:
•Predict YES iff (!recent delinq) AND (%down > 5).
•Predict YES iff 100*[mmp/inc] – 1*[%down] < 25.
•...

Big questions
(A) How might we automatically generate
rules that do well on observed data?
(B) What kind of confidence do we have
that they will do well in the future?
Or, in reverse order,
•What to optimize? [sample complexity]
•How to optimize it? [algorithms]

Power of basic paradigm
• E.g., document classification
– convert to bag-of-words
– LTFs do well
• E.g., driving a car
– convert image into
features.
– Use neural net with
several outputs.
Lots of problems solved by converting to basic
“concept learning from structured data” setting.

Stop 1: PAC model: Algs &
sample complexity

Natural formalization (PAC)
• Alg is given sample S = {(x,y)} presumed to
be drawn from some distribution D over
instance space X, labeled by some target
function f.
• Alg does optimization over S to produce
some hypothesis h.
• Goal is for h to be close to f over D.
• Allow failure with small prob d (to allow for
chance that S is not representative).

Basic PAC learning defn
• A concept class is a set of functions,
together with a representation of them.
• Alg A PAC-learns concept class C by
hypothesis class H if for any target f in C,
any distrib D over X, any e, d > 0,
– A uses at most poly(n,1/e,1/d,size(f)) examples
and running time.
– With probability 1-d, A produces h in H of
error at most e.
accuracy confidence

Example of guarantee: Decision Lists
Given a dataset S of m examples over n boolean
features, drawn according to unknown distrib
D, labeled by unknown target f:
1. Algorithm A will find a consistent DL if one
exists, in time O(mn).
2. If m > (1/e)[n(2+ln n) + ln(1/d)], then
Pr[exists consistent DL h with err(h) > e] < d.

How can we find a consistent DL?
if (x1=0) then -, else
if (x2=1) then +, else
if (x4=1) then +, else -

Decision List algorithm
• Start with empty list.
• Find if-then rule consistent with data.
(and satisfied by at least one example)
• Put rule at bottom of list so far, and cross off
examples covered. Repeat until no examples remain.
If algorithm fails, then:
•No DL consistent with remaining data.
•So, no DL consistent with original data.
OK, fine. Now why should we expect it
to do well on future data?

Confidence/sample-complexity
• Consider some hypothesis h with err(h) > e.
• Chance that h survives m examples is at
most (1-e)m.
• Number of DLs over n Boolean features is
at most n!4n. (for each feature there are 4 possible rules, and
no feature will appear more than once)
) Pr[some DL h with err(h)>e is consistent]
< n!4n(1-e)m.
• This is < d for m > (1/e)[n(2+ln n) + ln(1/d)]

Occam’s razor (contd)2
• Even if we have different notions of what’s
simpler (e.g., different representation
languages), we can both use Occam’s razor.
• Of course, there’s no guarantee there will
be a short explanation for the data. That
depends on your representation.
Nice interpretation:

Where are we now?
• Introduced PAC model. Showed class of
decision lists learnable by decision list
hypotheses.
• Saw Occam’s razor argument.
• Implications:
– If we view cost of examples as comparable
to cost of computation, then don’t have to
worry about cost of data since just need ~
1/e examples per bit we output.
– But, in practice, costs often wildly different,
so sample-complexity issues are crucial.

What’s next
• Stop 1(a): a few more words about sample
complexity.
• Stop 1(b): a few more words about
algorithmic issues.
• Stop 1(c): some of my favorite open
problems.

More on sample complexity
• Bounds so far are for “realizable case”. (when we
expect there will be a perfect rule). They say whp
if true error > e then empirical error > 0.
• More generally, might want to say whp all rules
have empirical error near to true error.
• “uniform convergence”
• Gives hope for local optimization over training data.

Tighter measures of size(H)
• Suppose we want to separate data using a
line in the plane.
+
+
+
+
-
-
-
-
• There are infinitely-many linear separators,
but not that many really different ones.
• In hindsight, only O(m2) ways of separating
our given m points.

Neat fact
• Can replace |H| with # ways of splitting
2m points in the bounds.
• # ways of splitting = O(mVC-dim(H)). (VC-
dimension is the largest number d of
points that can be split in all 2d ways.)
• We then take the log, so this shows the
number of examples needed is only linear
in VC-dim(H).

Proof sketch
Step 1: imagine we draw m training examples and m
test examples.
Then, Pr[exists h that looks good on train, bad on test]
> ½Pr[some bad h looks good on train]
(so can show 2nd is small by proving 1st is small)
Step 2: make even worse on ourselves by allowing
adversary to pick 2m points. Randomize over
partition into m train and m test.
Step 3: But now we’re back in finite case. Can
argue like Occam, over the splits of the sample.
Can view result as allowing description language to
depend on unlabeled double sample.

Other related nice bounds
• Margin bounds.
– Nice connection to Johnson-Lindenstrauss too.
• PAC-Bayes bounds.
– Can view Occam as saying for any prob dist P
over H, we can be confident in hi if it is
consistent with e-1[log(1/pi) + log(1/d)] samples.
(basically allowing hi to fool us with prob dpi)
– What if no individual hi that’s consistent has
high enough pi, but there are several that
together have a high sum of pi’s? Then can
randomize over them to get bounds as if was
one hyp with combined probs.
• Subsample (“compression”) bounds

Algorithmic/model issues
• If H=C then learning is NP-hard if
consistency problem is NP-hard.
– May not be clear if difficulty is due to C or H.
• To look at “how inherently hard is C to
predict?”, let H = poly time programs.
Then hardness is more cryptographic.
• To use hard learning problem for crypto,
need to be hard for “random” fns in C.
PAC model talks of learning C by H.

• In practice, most natural to fix H, allow C
to be arbitrary. (After all, H is under
our control, but target function isn’t).
• Try to find reasonable h in H if one
exists. Usually hard to say anything
positive theoretically.
PAC model talks of learning C by H.

• Get same set of learnable concepts if fix
e=1/4, d = ½. (reasonable prob, reasonable acc)
• Or, e = ½ - 1/poly, d = 1 – 1/poly. (With
1/poly chance we do slightly better than
random guessing). “weak learning”.
• Result for d is easy to see. Result for e is
Boosting. [Schapire, Freund & Schapire]
– Boosting results are really nice (both theoretically and
practically). Involves re-weighting sample and rerunning
algorithm. But not going to talk about....
PAC model talks of e and d. (high prob, high acc)

• “Membership query” model: learner can ask
for f(x) for examples x of its construction.
– adds significant power.
– allows for more interesting algorithms.
– but rare in practice. Esp because “target
function” is really a fiction.
• What is often possible in practice is “active
learning”. Have large unlabeled sample and
alg may choose among these.
What about adding extra power to alg?

Open Problems
1. Learning functions of r relevant variables.
2. Learning “almost-OR” functions.
3. Learning DLs over string-valued features.
4. Learning Monotone DNF over uniform.

Learning functions of r variables
• Examples in {0,1}n. Uniform distribution.
• But only r of these n bits are relevant.
• Target is arbitrary function of these r.
Q1: learn in poly time for r = O(log n)?
Note: this is special case of “Are DNF learnable
over uniform dist?” and “Are decision trees
learnable over uniform dist?”

Q1: learn in poly time for r = O(log n)?
Note 2: this is easy if we allow Membership
Queries.

Q2: How about r = loglog(n)?
Now can assume we know truth-table, since only
n possibilities.

Q2: How about no(r) or even nr/2?
Best known is recent [MOS] result of ~ n0.7r.

Example of hard-looking function
• Pick two sets A,B.
• Target is maj(A) XOR parity(B).

Learning “almost-OR” functions
Suppose target f has the property that it is
close to an OR function over D.
– for some OR f’, Prx[f(x) != f’(x)] < 1%.
Can we get error < 49%? ½-1/nk? (in poly time)
If we replace “OR” with “XOR”, and require
H=C, then [Hastad] shows ½ - e is NP-hard.
[Note: new hardness result of Dinur, Guruswami, and Khot on Set-Cover implies
our problem is NP-hard if we require hyp to be an OR-function *and* require 1-
sided error (we must get all positives correct, and at least 1% of negatives
correct)]

Possibly easier version
Suppose really two functions, f and g:
– f is an OR function.
– g is arbitrary.
– Example drawn from D. With probability 99%,
gets labeled by f, with probability 1%, gets
labeled by g.
Potentially easier since adversary has less
control. Still open though.
However, if require g = 1-f, then this is random
noise model, and problem is easy. (even if
you raise noise rate to 49%).

Learning Decision Lists revisited
• What if features were string-valued
rather than boolean valued? [also known as
“infinite attribute model”]
• E.g., x = (hello, how, are, you).
• Target is a decision list, like:
if (x1 = “hello”) then +, else if (x2 = “joe”) then
-, else if (x1 = “aargh”) then +, else -.
• Can we learn in time poly(n, size(f))?
(Previous alg may produce hyp that grows
linearly with size of data set.)

Learning Monotone DNF over uniform
• Uniform distribution on {0,1}n.
• Target is a monotone DNF formula.
• Can you learn with error rate < 25%?
It’s known how to achieve error rate ½ -
O(n-1/2) [weak learning] [BBL]
It’s known how to strong-learn if the
number of terms is small: 2p(log n) [S]
But it’s not known how to strong-learn in
general.

Basic setting
• View learning as a sequence of trials.
• In each trial, algorithm is given x, asked to
predict f, and then is told the correct value.
• Make no assumptions about how examples
are chosen.
• Goal is to minimize number of mistakes.
Note: can no longer talk about # examples needed
to converge. Instead, we focus on number of
mistakes. Need to “learn from our mistakes”.

Simple example: learning an OR fn
• Suppose features are boolean: X = {0,1}n.
• Target is an OR function, like x3 v x9 v x12,
with no noise.
• Can we find an on-line strategy that makes
at most n mistakes?
• Sure.
– Start with h(x) = x1 v x2 v ... v xn
– Invariant: {vars in h} contains {vars in f }
– Mistake on negative: throw out vars in h set to 1
in x. Maintains invariant and decreases |h| by 1.
– No mistakes on postives. So at most n mistakes
total.

More general class: LTFs
• Target is a vector (a1, ... an) and threshold t.
• Example x in {0,1}n is positive if a1x1 + ... + anxn > t,
else is negative.
• An OR function is the case ai in {0,1}, t=1.
Q: can you guarantee at most poly(n,size(f)) mistakes?
Yes. Use the ellipsoid algorithm.
• Examples seen so far form a set of linear
constraints. (reversing usual use of “a” and “x” in LP)
• Center of ellipsoid is hypothesis.
• Mistake can be viewed as output of separation
oracle.

Using “expert” advice
• We solicit n “experts” for their advice. (Will the
market go up or down?)
• We then want to use their advice somehow to
make our prediction. E.g.,
Say we want to predict the stock market.
Rough question: What's a good strategy for using their
opinions, given that in advance we don't know which is best?
[“expert” ´ someone with an opinion. Not necessarily
someone who knows anything.]

Simpler question
• We have n “experts”.
• One of these is perfect (never makes a mistake).
We just don’t know which one.
• Can we find a strategy that makes no more than
lg(n) mistakes?
Answer: sure. Just take majority vote over all experts that
have been correct so far. Called “halving algorithm”.
Followup question: what if we have a “prior” p over the
experts. Can we make no more than lg(1/pi) mistakes, where
expert i is the perfect one?
Sure, just take weighted vote according to p.

Relation to concept learning
• If computation time is no object, can have
one “expert” per concept in C.
• If target in C, then number of mistakes at
most lg(|C|).
• More generally, for any description
language, number of mistakes is at most
number of bits to write down f.

Back to expert-advice
What if no expert is perfect? Goal is to do nearly
as well as the best one in hindsight.
Strategy #1:
• Iterated halving algorithm. Same as before, but
once we've crossed off all the experts, restart
from the beginning.
• Makes at most log(n)*OPT mistakes, where OPT is
# mistakes of the best expert in hindsight.
Seems wasteful. Constantly forgetting what we've
“learned”. Can we do better? Yes.

Weighted Majority Algorithm
Intuition: Making a mistake doesn't completely
disqualify an expert. So, instead of crossing
off, just lower its weight.
Weighted Majority Alg:
– Start with all experts having weight 1.
– Predict based on weighted majority vote.
– Penalize mistakes by cutting weight in half.

Weighted Majority Algorithm
Weighted Majority Alg:
– Start with all experts having weight 1.
– Predict based on weighted majority vote.
– Penalize mistakes by cutting weight in half.
Example:

Analysis: do nearly as well as best
expert in hindsight
• M = # mistakes we've made so far.
• m = # mistakes best expert has made so far.
• W = total weight (starts at n).
• After each mistake, W drops by at least 25%.
So, after M mistakes, W is at most n(3/4)M.
• Weight of best expert is (1/2)m. So,
constant
comp. ratio

Randomized Weighted Majority
2.4(m + lg n) not so good if the best expert makes a
mistake 20% of the time. Can we do better? Yes.
• Instead of taking majority vote, use weights as
probabilities. (e.g., if 70% on up, 30% on down, then pick
70:30) Idea: smooth out the worst case.
• Also, generalize ½ to 1- e.
unlike most C.R.s
or apx bounds,
numbers are
pretty good.

Analysis
• Say at time t we have fraction Ft of weight on
experts that made mistake.
• So, we have probability Ft of making a mistake, and
we remove an eFt fraction of the total weight.
– Wfinal = n(1-e F1)(1 - e F2)...
– ln(Wfinal) = ln(n) + t [ln(1 - e Ft)] · ln(n) - e t Ft
(using ln(1-x) < -x)
= ln(n) - e M. ( Ft = E[# mistakes])
• If best expert makes m mistakes, then ln(Wfinal) > ln((1-e)m).
• Now solve: ln(n) - e M > m ln(1-e).

A fun application
• n buckets. (Think of as startup companies.)
• You are standing in one of them.
• At each time step, a ball falls into one of the buckets. If
it's your bucket, you get $1.
• Then you can choose to move if you want
• Game ends when fullest bucket has d balls.
This is hopeless if an opponent is tossing the balls based on
knowing where you are(n't). However, say sequence of balls
is predetermined (but unknown).
Randomized WM will guarantee you an expected gain of at
least d – (2d log n)1/2.
[Multiply weight by 1+e whenever ball falls in, e = (d/log n)1/2.]

Summarizing
• Can be (1+e)-competitive with best expert
in hindsight, with additive e-1log(n).
• If have prior, can replace additive term
with e-1log(1/pi). [e-1 x number of bits]
• Often written in terms of additive loss.
If running T time steps, set epsilon to get
additive loss (2T log n)1/2

What can we use this for?
• Can use to combine multiple algorithms to
do nearly as well as best in hindsight.
– E.g., online auctions: one expert per price level.
• Play repeated game to do nearly as well as
best strategy in hindsight (which is at least
as good as minimax optimal).
• Extensions: “bandit problem”, movement
costs.

What about if “n” is large?
• Bounds still good even if #experts is very
large.
– adaptive search trees: one expert per tree.
– online path planning: one expert per path.
– online linear programming: one expert per
corner.
• Nice recent results [KV][Z] on ways to get
these bounds efficiently for these types
of problems.

Stop 3: SQ learning and
Fourier analysis

What are the goals of
complexity theory?
• We’d like to prove w/o assumptions that
no polynomial-time algorithm can solve
some natural problem (like one in NP).
• Barring that, how about an algorithm
with one arm tied behind its back?
• How about both arms behind back, while
hopping on one foot? (like AC0 ckts)
How often do you find yourself saying: “drat, there’s no way
my alg is going to succeed because it’s an AC0 ckt”?

What are the goals of
complexity theory?
• We’d like to prove w/o assumptions that
no polynomial-time algorithm can solve
some natural problem (like one in NP).
• Barring that, how about an algorithm
with one arm tied behind its back?
• How about both arms behind back, while
hopping on one foot? (like AC0 ckts)
(Of course, there’s been phenomenal success in showing
how mild-looking assumptions can have enormous
consequences)

SQ and Fourier analysis
• We will define a paradigm for using
data that nearly all learning algorithms
satisfy or can be cast into. (“Statistical
Queries”)
• Use Fourier analysis to prove without
assumptions they cannot learn certain
natural classes in poly time.
– E.g., decision trees, parity functions,
log(n)-relevant-var function.

The Statistical Query model
• PAC model, but algorithm no longer has
access to individual labeled examples.
• Instead, algorithm may ask “what is the
probability a labeled example would have
property P?” Gets answer back up to error t.
• Think of this as asking for statistics about a
poly-size sample S.
• Formally, P must be poly-time computable,
and t > 1/poly(...).
• Also assume alg knows underlying dist D.

Most algorithms can be cast
in this framework
E.g., list-and-cross-off OR-function alg:
• Ask for Pr[f(x)=0 & xi = 1] with t = e/2n.
(i.e., what’s the chance we’d cross it off)
• Cross off all xi with Pr[..] > e/2n.
• What’s left will include all correct variables.
• Might include some incorrect ones too, but
they will introduce at most e/n error each.

in this framework
• Given current hypothesis h.
• Asks for err(h) = Pr[h(x) != f(x)].
• Then asks: “what if I make small change z or
tweak parameter w, does it get better?”
E.g., typical local-optimization algorithm:

in this framework
In fact, only recently were examples shown of
(non-SQ) alg to learn something with noise
that is not learnable by SQs.
What I like: can get very good handle on what
can/cannot be learned in this model.
Original motivation of model: algorithms
that behave this way can automatically be
made tolerant to random classification
noise. [Kearns]

Fourier analysis of finite fns
• Let’s write pos, neg as +1, -1.
• Think of a fn f:{0,1}n! {-1,+1} as vector:
Nice properties:
• <f,f> = x PrD(x)f(x)f(x) = 1.
• <f,g> = x PrD(x)f(x)g(x)
= PrD[f(x) = g(x)] – PrD[f(x) != g(x)].
“orthogonal” = “pairwise uncorrelated”. E.g.,
under uniform dist, all 2n parity functions
are orthogonal (so they form a basis).

SQ dimension
SQ dimension of a concept class C, over distr
D, is the size of the largest subset S of C
of “nearly orthogonal” functions:
– For all f,g in S, |<f,g>| < 1/|S|.
• If SQ-dim(C) = poly(...) then you can weak-
learn over D in SQ model. [non-uniform alg]
• If SQ-dim(C) > poly(...) then you can’t weak-
learn over D in SQ model.

Positive direction
If SQ-dim(C) = poly(...) then you can weak-
– SQ dimension of a concept class C, over distr D,
is the size of the largest subset S of C such that
for all f,g in S, |<f,g>| < 1/|S|.
– So, just hard-code S = {h1,...,h|S|} into the
learning alg.
– Ask for correlation of hi with target for all hiin S
– One of them must have at least 1/|S| correlation

Negative direction
If SQ-dim(C) > poly(...) then you can’t weak-
Slightly over-simplified proof sketch:
– Show that any statistical query can be converted
to asking for correlation of target with some
unit-length vector q, up to +/- 1/poly.
– So, suppose C has > poly(...) orthogonal functions.
– query q can have correlation e with at most 1/e2
of them.
– If only ask poly many queries, can only knock out
negligible fraction. If target is random from S,
then whp all queries can be answered with 0.

Implications
• Parity functions have SQ-dim 2n, so can’t
learn by SQ.
• Decision trees contain {Parity functions of
size log(n)}. nlog n of them, so can’t learn by
SQ.
• Same for “functions of log(n) relevant
variables.”

Stop 4: Current “hot”
practical issues

Current “hot” practical issues
• Learning from labeled and unlabeled data.
• Nonlinear embeddings.
• MDPs and stochastic games.
• Adaptive programs and environments.
• Kernels, comp bio, many others, ... [not
going to talk about]

Learning from labeled and
unlabeled data
• Often unlabeled data is cheap, labeled data is
expensive. Can unlabeled data help?
• Rough answer: unlabeled data can suggest which
hyps are a-priori more reasonable.
– E.g., linear separators that don’t go through clusters.
– E.g., data with pair-wise relationships (vertices in a
graph): hyps that are good cuts or good ratio-cuts.
Using unlabeled data to order your hypotheses.
• Algorithmically, can use to bootstrap.
– E.g., co-training if two different sources of info. (e.g.,
words on page; words pointing to page)

Nonlinear embeddings
• Often the “real” dimensionality of data is a
lot smaller than the space we’re using.
• But might not be just a linear embedding.
• Want a nonlinear projection that unrolls
the manifold.
• E.g., one approach: take a lot of data, draw
nearest-neighbor graph, use to project.

MDPs and stochastic games.
• Robot learning to act in its environment.
– Think of a directed graph where edges have
rewards and a probability distribution over
endpoints. (MDP)
• Robot learning to act in environment with
other agents who have their own agendas.
– Stochastic game.

Adaptive programs and
environments
• Can machine learning be a first-class part
of a programming language?
– E.g., easy to write code with parameters that
get optimized to the user’s needs.
• Can systems/environments be built that
adapt, suggest, help, etc.
• Mostly crude (paperclip) or just talk right
now...

References
[need to fill in...]
For more information, there is a web site for the
area as a whole at www.learningtheory.org, with
pointers to survey articles, course notes,
tutorials, and textbooks.

tutorial.ppt

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