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Correlation Immune Functions and Learning. Lisa Hellerstein Polytechnic Institute of NYU Brooklyn, NY Includes joint work with Bernard Rosell (AT&T), Eric Bach and David Page (U. of Wisconsin), and Soumya Ray (Case Western). Identifying relevant variables from random examples.

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correlation immune functions and learning

Correlation Immune Functions and Learning

Lisa Hellerstein

Polytechnic Institute of NYU

Brooklyn, NY

Includes joint work with Bernard Rosell (AT&T), Eric Bach and David Page (U. of Wisconsin), and Soumya Ray (Case Western)

identifying relevant variables from random examples
Identifying relevant variables from random examples

x f(x)

(1,1,0,0,0,1,1,0,1,0) 1

(0,1,0,0,1,0,1,1,0,1) 1

(1,0,0,1,0,1,0,0,1,0) 0

technicalities
Technicalities
  • Assume random examples drawn from uniform distribution over {0,1}n
  • Have access to source of random examples
detecting that a variable is relevant
Detecting that a variable is relevant
  • Look for dependence between input variables and output

If xi irrelevant P(f=1|xi=1) = P(f=1|xi=0)

If xi relevant P(f=1|xi=1) ≠ P(f=1|xi=0)

for previous function f

unfortunately
Unfortunately…

xi relevant P(f=1|xi=1) = 1/2 = P(f=1|xi=0)

xi irrelevant P(f=1|xi=1) = 1/2 = P(f=1|xi=0)

Finding a relevant variable easy for some functions.

Not so easy for others.

how to find the relevant variables
How to find the relevant variables
  • Suppose you know r (# of relevant vars)

Assume r << n

(Think of r = log n)

  • Get m random examples, where

m = poly(2r ,log n,1/δ)

  • With probability > 1-δ, have enough info to determine which r variables are relevant
    • All other sets of r variables can be ruled out
slide7

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10f

(1, 1, 0, 1, 1, 0, 1, 0, 1, 0) 1

(0, 1, 1, 1, 1, 0, 1, 1, 0, 0) 0

(1, 1, 1, 0, 0, 0, 0, 0, 0, 0) 1

(0, 0, 0, 1, 1, 0, 0, 0, 0, 0) 0

(1, 1, 1, 0, 0, 0, 1, 1, 1, 1) 0

slide8

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10f

(1, 1, 0, 1, 1, 0, 1, 0, 1, 0) 1

(0, 1, 1, 1, 1, 0, 1, 1, 0, 0) 0

(1, 1, 1, 0, 0, 0, 0, 0, 0, 0) 1

(0, 0, 0, 1, 1, 0, 0, 0, 0, 0) 0

(1, 1, 1, 0, 0, 0, 1, 1, 0, 1) 0

slide9

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10f

(1, 1, 0, 1, 1, 0, 1, 0, 1, 0) 1

(0, 1, 1, 1, 1, 0, 1, 1, 0, 0) 0

(1, 1, 1, 0, 0, 0, 0, 0, 0, 0) 1

(0, 0, 0, 1, 1, 0, 0, 0, 0, 0) 0

(1, 1, 1, 0, 0, 0, 1, 1, 0, 1) 0

x3, x5, x9 can’t be the relevant variables

slide10

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10f

(1, 1, 0, 1, 1, 0, 1, 0, 1, 0) 1

(0, 1, 1, 1, 1, 0, 1, 1, 0, 0) 0

(1, 1, 1, 0, 0, 0, 0, 0, 0, 0) 1

(0, 0, 0, 1, 1, 0, 0, 0, 0, 0) 0

(1, 1, 1, 0, 0, 0, 1, 1, 1, 1) 0

x1, x3, x10 ok

slide11
Naïve algorithm: Try all combinations of r variables. Time ≈ nr
  • Mossel, O’Donnell, Servedio [STOC 2003]
    • Algorithm that takes time ≈ ncr where c ≈ .704
    • Subroutine: Find a single relevant variable

Still open: Can this bound be improved?

slide12
If output of f is dependent on xi, can detect dependence (whp) in time poly(n, 2r) and identify xi as relevant.

Problematic Functions

Every variable is independent of output of f

P[f=1|xi=0] = P[f=1|xi=1] for all xi

Equivalently, all degree 1 Fourier coeffs = 0

Functions with this property said to be

CORRELATION-IMMUNE

slide13
P[f=1|xi=0] = P[f=1|xi=1] for all xi

Geometrically:

11

10

e.g. n=2

01

00

slide14
P[f=1|xi=0] = P[f=1|xi=1] for all xi

Geometrically:

0

1

11

10

Parity(x1,x2)

01

00

0

1

slide15
P[f=1|xi=0] = P[f=1|xi=1] for all xi

Geometrically:

0

1

11

10

X1=1

X1=0

01

00

0

1

slide17
Other correlation-immune functions besides parity?
    • f(x1,…,xn) = 1 iff x1 = x2 = … = xn
correlation immune functions and decision tree learners
Correlation-immune functions and decision tree learners
  • Decision tree learners in ML
    • Popular machine learning approach (CART, C4.5)
    • Given set of examples of Boolean function, build a decision tree
  • Heuristics for decision tree learning
    • Greedy, top-down
    • Differ in way choose which variable to put in node
    • Pick variable having highest “gain”
    • P[f=1|xi=1] = P[f=1|xi=0] means 0 gain
  • Correlation-immune functions problematic for decision tree learners
slide21
Lookahead
  • Skewing: An efficient alternative to lookahead for decision tree induction. IJCAI 2003 [Page, Ray]
  • Why skewing works: learning difficult Boolean functions with greedy tree learners. ICML 2005 [Rosell, Hellerstein, Ray, Page]
slide22

Story

Part One

slide23

n

  • How many difficult functions?
  • More than

# fns

n-1

2

2

slide24

n

  • How many different hard functions?
  • More than

SOMEONE MUST HAVE STUDIED THESE FUNCTIONS BEFORE…

# fns

n/2

2

2

slide27

Story

Part Two

slide29
Roy, B. K. 2002. A Brief Outline of Research on Correlation Immune Functions. In Proceedings of the 7th Australian Conference on information Security and Privacy (July 03 - 05, 2002). L. M. Batten and J. Seberry, Eds. Lecture Notes In Computer Science, vol. 2384. Springer-Verlag, London, 379-394.
correlation immune functions
Correlation-immune functions
  • k-correlation immune function
    • For every subset S of the input variables s.t.

1 ≤ |S| ≤ k

P[f | S] = P[f]

    • [Xiao, Massey 1988] Equivalently, all Fourier coefficients of degree i are 0, for

1 ≤ i ≤ k

slide31
Siegenthaler’s Theorem

If f is k-correlation immune, then the GF[2] polynomial for f has degree at most n-k.

slide32
Siegenthaler’s Theorem [1984]

If f is k-correlation immune, then the GF[2] polynomial for f has degree at most n-k.

Algorithm of Mossel, O’Donnell, Servedio [STOC 2003] based on this theorem

non uniform distributions
Non-uniform distributions
  • Correlation-immune functions are defined wrt the uniform distribution
  • What if distribution is biased?

e.g. each bit 1 with probability ¾

f x 1 x 2 parity x 1 x 2 p 1 with probability 1 4
f(x1,x2) = parity(x1,x2)p=1 with probability 1/4

P[f=1|x1=1] ≠ P[f=1|x1=0]

For added irrelevant variables, would be equal

correlation immunity wrt p biased distributions
Correlation-immunity wrt p-biased distributions

Definitions

  • f is correlation-immune wrt distributionD if

PD[f=1|xi=1] = PD[f=1|xi=0]

for all xi

  • p-biased distribution Dp: each bit set to 1 independently with probability p
    • For all p-biased distributions D,

PD[f=1|xi=1] = PD[f=1|xi=0]

for all irrelevant xi

slide38
Lemma: Let f(x1,…,xn) be a Boolean function with r relevant variables. Then f is correlation immune w.r.t. Dp for at most r-1 values of p.

Pf: Correlation immune wrt Dp means

P[f=1|xi=1] – P[f=1|xi=0] = 0 (*)

for all xi.

Consider fixed f and xi. Can write lhs of (*)

as polynomial h(p).

slide39
e.g. f(x1,x2, x3) = parity(x1,x2, x3)p-biased distribution Dp

h(p) = PDp[f=1|x1=1] - PDp[f=1|x1=0] =

( p2 + p(1-p) ) – ( p(1-p) + (1-p)p )

If add irrelevant variable, this polynomial doesn’t change

  • h(p) for arbitrary f, variable xi, has degree <= r-1, where r is number of variables.
  • f correlation-immune wrt at most r-1 values of p, unless h(p) identically 0 for all xi.
slide40
h(p) = PDp[f=1|xi=1] -PDp[f=1|xi=0]

where wd is number of inputs x for which f(x)=1, xi=1, and x contains exactly d additional 1’s.

i.e. wd = number of positive assignments of fxi<-1 of Hamming weight d

  • Similar expression for PDp[f=1|xi=0]
slide41
PDp[f=1|xi=1] - PDp[f=1|xi=0]

=

where wd = number of positive assignments of fxi<-1 of Hamming weight d

rd = number of positive assignments of fxi<-0 of Hamming weight d

Not identically 0 iff wd≠ rd for some d

property of boolean functions
Property of Boolean functions

Lemma: If f has at least one relevant variable, then for some relevant variable xi, and some d,

wd≠ rd for some d

where

wd = number of positive assignments of fxi<-1 of Hamming weight d

rd = number of positive assignments of fxi<-0 of Hamming weight d

slide44
How much does it help to have access to examples from different distributions?

Hellerstein, Rosell, Bach, Page, Ray

Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions

Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions [Hellerstein, Rosell, Bach, Ray, Page]

slide45
Even if f is not correlation-immune wrt Dp, may need very large sample to detect relevant variable
    • if value of p very near root of h(p)
  • Lemma: If h(p) not identically 0, then for some value of p in the set

{ 1/(r+1),2/(r+1),3/(r+1)…, (r+1)/(r+1) },

h(p) ≥ 1/(r+1)r-1

slide46
Algorithm to find a relevant variable
    • Uses examples from distributions Dp, for

p = 1/(r+1),2/(r+1),3/(r+1)…, (r+1)/(r+1)

    • sample size poly((r+1) r, log n, log 1/δ)

[Essentially same algorithm found independently by Arpe and Mossel, using very different techniques]

  • Another algorithm to find a relevant variable
    • Based on proving (roughly) that if choose random p, then h2(p) likely to be reasonably large. Uses prime number theorem.
    • Uses examples from poly(2r, log 1/ δ) distributions Dp.
    • Sample size poly(2r, log n, log 1/ δ)
summary
Summary
  • Finding relevant variables (junta-learning)
  • Correlation-immune functions
  • Learning from p-biased distributions
moral of the story
Moral of the Story
  • Handbook of integer sequences can be useful in doing literature search
  • Eating lunch with the right person can be much more useful
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