Correlation Distillation

Introduction

Correlation Distillation

Elchanan Mossel

April 13, 2015

Elchanan Mossel Correlation Distillation

Introduction Motivation

Executive Summary

To distill correlation you need to be stable.

Sometime balls, sometimes cubes are more able.

But in Gaussian space - we don’t know - you ask why?

It’s because the optimal partition in not always a Y.



Executive Summary



But in Gaussian space - we don’t know - you ask why?




The Correlation Distillation Problem

Let X and Y be two random variables and µ a distribution on[q].

Goal: find f : ΩX → [q], g : ΩY → [q] such that

f (X ), g(Y ) ∼ µ and P[f (X ) = f (Y )] is maximized.

Can be formulated as a question about noise stability.

Or a Shannon Theory problem: decoding randomness from aphysical source.

Motivation 2: hardness of approximation (Hastad, Khot etc.)

Motivation 3: robustness of voting (Kalai)

Motivation 4: communication complexity (Canonne-Guruwami-Meka-Sudan-14).

If there’s time left - also something about tail spaces.



























Correlation Distillation - known cases

If q = 2 and X ,Y ∼ N(0, In) with E[XTY ] = ρIn and ρ > 0:

Borell-85: Optimum is f = g = indicator of a half space.

If q = 2, µ = 0.5(δ−1 + δ1) and X ,Y ∈ −1, 1n with X andY ρ-correlated.

Folklore: f = g = x1 is optimal.

These are essentially the only cases known exactly.



















Correlation Distillation and hypercontraction

Let X ,Y be ρ = (1 + ε)/2 correlated in 0, 1n.

Let µ be uniform on 0, 1k .

f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε)k ∼ e−εk .

How tight is the cube partition?

Bogdanov-Mossel-12: By hyper-contractivity:P[f (X ) = g(Y )] =

∑z P[f (X ) = g(Y ) = z ]

≤ 2k‖1(f = z)‖21+ρ = 2k(ρ−1)/(ρ+1) = 2−kε/(1−ε) ∼ 2−εk .

BM12: P[f (X ) = f (Y )] ≥ 0.1(kε)−1/22−kε/(1−ε) for f =partition of cube to Hamming balls.

Ball Partitions are better than Cube Partitions!






f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε)k ∼ e−εk .



∑z P[f (X ) = g(Y ) = z ]

≤ 2k‖1(f = z)‖21+ρ = 2k(ρ−1)/(ρ+1) = 2−kε/(1−ε) ∼ 2−εk .








f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε)k ∼ e−εk .



∑z P[f (X ) = g(Y ) = z ]

≤ 2k‖1(f = z)‖21+ρ = 2k(ρ−1)/(ρ+1) = 2−kε/(1−ε) ∼ 2−εk .








f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε)k ∼ e−εk .



∑z P[f (X ) = g(Y ) = z ]

≤ 2k‖1(f = z)‖21+ρ = 2k(ρ−1)/(ρ+1) = 2−kε/(1−ε) ∼ 2−εk .








f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε)k ∼ e−εk .



∑z P[f (X ) = g(Y ) = z ]

≤ 2k‖1(f = z)‖21+ρ = 2k(ρ−1)/(ρ+1) = 2−kε/(1−ε) ∼ 2−εk .





From binary to q-ary

Let X ,Y be ρ = (1− ε) correlated in [q]n.

Let µ be uniform on [q]k .

f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε+ ε/q)k


Chan-Mossel-Neeman-13: using hyper-contractivity:

P[f (X ) = g(Y )] ≤ (1− ε)k(1 + δ(q))k , δ(q)→q→∞ 0.

So cube partitions are tight as q →∞.

CNM-13: Any construction based on Hamming balls satisfies:

P[f (X ) = f (Y )] ≤ q−ckε, c > 0.

Cube Partitions are better than Ball partitions!






f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε+ ε/q)k



P[f (X ) = g(Y )] ≤ (1− ε)k(1 + δ(q))k , δ(q)→q→∞ 0.



P[f (X ) = f (Y )] ≤ q−ckε, c > 0.







f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε+ ε/q)k



P[f (X ) = g(Y )] ≤ (1− ε)k(1 + δ(q))k , δ(q)→q→∞ 0.



P[f (X ) = f (Y )] ≤ q−ckε, c > 0.







f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε+ ε/q)k



P[f (X ) = g(Y )] ≤ (1− ε)k(1 + δ(q))k , δ(q)→q→∞ 0.



P[f (X ) = f (Y )] ≤ q−ckε, c > 0.







f = g = xk1 =⇒ P[f (X ) = g(Y )] = (1− ε+ ε/q)k



P[f (X ) = g(Y )] ≤ (1− ε)k(1 + δ(q))k , δ(q)→q→∞ 0.



P[f (X ) = f (Y )] ≤ q−ckε, c > 0.




Correlation Distillation in Gaussian Space

Theorem (Borell, Tsirelson-Sudakov ’75)

In Gaussian space, the sets of a given measure that minimizeGaussian surface area are half-spaces.

Theorem ( Borell ’85)

In Gaussian space, sets of a given measure that maximize noisestability are half-spaces.

Corollary

If µ is a measure on 2 points and X ,Y ∼ N(0, I ) are ρ > 0correlated then f = g = half space is an optimal solution tocorrelation distillation.








Corollary









Corollary




The Euclidean Picture

Theorem (Archimedes(-200**), Schwartz (18**))

The body on given measure and minimal surface area is a ball.

Theorem (Plateau, Boys (18**), Hutching Morgan Ritoro Ros(2002))

In the case of two bodies the answer is double bubble.



The Euclidean Picture

Theorem (Archimedes(-200**), Schwartz (18**))

The body on given measure and minimal surface area is a ball.

Theorem (Plateau, Boys (18**), Hutching Morgan Ritoro Ros(2002))

In the case of two bodies the answer is double bubble.



The Ys?

Theorem (Corneli, Corwin, Hurder, Sesum, Xu, Adams, Davis, Lee,Visocchi, Hoffman ’08)

For q = 3 and f : Rn → [3] with 0.332 ≤ P[f = a] ≤ 0.334 ∀a, theShifted Y minimizes Gaussian surface area.

Theorem (Heilman 13)

If µ is uniform over [3] and n < n(ρ) then standard Y is a solutionto the correlation distillation problem.

Theorem (Heilam-Mossel-Neeman-14)

For every µ 6= (1/3, 1/3, 1/3) on [3] and any ρ ∈ (0, 1), shifted Y sin Gaussian space are not a solution of the correlation distillationproblem.



The Ys?









The Ys?









A Shifted Simplex

B1 + y B2 + y

0

y

B3 + y



And the balanced case?

Still don’t know.

Borell =⇒ simplex partitions are optimal up to a constantfactor for any q (KKMO-07).

Heilman-13: Standard simplexes are most stable in Rn forbounded dimensions. n ≤ n(ρ).

So far: no techniques / intuitions on what to do if that’s thecase.




Still don’t know.







Still don’t know.






Sketch of the proof that shifted Y are not optimal

WLOG assume one arm parallel but not equal to y axis.

By a first variation argument it suffices to show thatTρ(1B1+y − 1B2+y )(x) is not constant forx ∈ (B1 + y) ∩ (B2 + y).

Let f (t) := Tρ(1(B1+y) − 1(B2+y)) restricted to the lineseparating B1 and B2. Then

|limt→+∞ f (t)| = 2γ1[0, c], where c(ρ) 6= 0.

limt→−∞ f (t) = 0.f (t) is a holomorphic function of t for all complex t.







|limt→+∞ f (t)| = 2γ1[0, c], where c(ρ) 6= 0.limt→−∞ f (t) = 0.

f (t) is a holomorphic function of t for all complex t.







|limt→+∞ f (t)| = 2γ1[0, c], where c(ρ) 6= 0.

limt→−∞ f (t) = 0.f (t) is a holomorphic function of t for all complex t.







|limt→+∞ f (t)| = 2γ1[0, c], where c(ρ) 6= 0.limt→−∞ f (t) = 0.

f (t) is a holomorphic function of t for all complex t.







|limt→+∞ f (t)| = 2γ1[0, c], where c(ρ) 6= 0.limt→−∞ f (t) = 0.f (t) is a holomorphic function of t for all complex t.



Sketch of the proof

The lastassertion isnew inisoperimetrictheory.

The first twoassertions havethe followingpicture inmind.

B1 + y B2 + y

L0

t →∞

B1 + y B2 + y

y



Sketch of the proof



B1 + y B2 + y

L0

t →∞

B1 + y B2 + y

y



Sketch of the proof



B1 + y B2 + y

L0

t →∞

B1 + y B2 + y

y



Executive Summary



But in Gaussian space- we don’t know - you ask why?




Open Problems

Find better correlation distillation for

Gaussian space q ≥ 3.0, 1n → 0, 1k (improve polynomial factors).[q]n → [q]k (get the right exponent for every q).Other correlated variables.

When do there exist sets / small sets which are tight forhypercontractive / Log-Sob inequalities.



Open Problems

Find better correlation distillation for

Gaussian space q ≥ 3.0, 1n → 0, 1k (improve polynomial factors).[q]n → [q]k (get the right exponent for every q).Other correlated variables.

When do there exist sets / small sets which are tight forhypercontractive / Log-Sob inequalities.



Executive Summary - Part 2

Based on Joint work with Steven Heilman and KrzysztofOleszkiewicz.

The tale of the tail:

Tails diminish faster - so it seems.

Yet their influence may be dim.

And their boundaries almost unseen.




Based on Joint work with Steven Heilman and KrzysztofOleszkiewicz.







Boolean Functions and Tail Spaces

The Fourier expansion of f : −1, 1n → R is:

f (x) =∑S⊆[n]

f (S)xS , xS =∏i∈S

xi .

L>k := f : f (S) = 0, ∀ |S | ≤ k.L>k+ := f : f (S) = 0, ∀ 0 < |S | ≤ k

what information can be extracted from f ∈ L>k .

Note in particular - f ∈ L>k stronger than ”superconcentration”.





f (x) =∑S⊆[n]


xi .

L>k := f : f (S) = 0, ∀ |S | ≤ k.

L>k+ := f : f (S) = 0, ∀ 0 < |S | ≤ k







f (x) =∑S⊆[n]


xi .

L>k := f : f (S) = 0, ∀ |S | ≤ k.L>k+ := f : f (S) = 0, ∀ 0 < |S | ≤ k







f (x) =∑S⊆[n]


xi .

L>k := f : f (S) = 0, ∀ |S | ≤ k.L>k+ := f : f (S) = 0, ∀ 0 < |S | ≤ k







f (x) =∑S⊆[n]


xi .

L>k := f : f (S) = 0, ∀ |S | ≤ k.L>k+ := f : f (S) = 0, ∀ 0 < |S | ≤ k





The Bonami-Beckner Operator and Contraction

Pt re-randomized each coordinate with probability 1− e−t :

(Pt f )(x) := E [f (y)|y ∼e−t x ] =∑S

e−t|S |f (S)xS

Clearly, ‖Pt f ‖2 ≤ e−tk‖f ‖2 for f ∈ L≥k .

Mendel and Naor: What about other norms?

Motivation: Study of ”super-expanders” (with respect to allconvex spaces).





(Pt f )(x) := E [f (y)|y ∼e−t x ] =∑S

e−t|S |f (S)xS








(Pt f )(x) := E [f (y)|y ∼e−t x ] =∑S

e−t|S |f (S)xS








(Pt f )(x) := E [f (y)|y ∼e−t x ] =∑S

e−t|S |f (S)xS






Contraction in Tail Spaces

For f ∈ L≥k and p > 1:

‖Pt f ‖p ≤ e−c(p)k min(t,t2)‖f ‖p, p ≥ 2, Meyer, Mendel-Naor

‖Pt f ‖p ≤ e−c(p)kt‖f ‖p, p > 1, Conj: Mendel-Naor

‖Pt f ‖p ≤ e−c(p)kt‖f ‖p, if ∀x , f (x) ∈ −1, 0, 1 HMO



An Easy Proof

For f ∈ L≥k :

‖Pt f ‖p ≤ e−c(p)kt‖f ‖p, p > 1, f ∈ −1, 0, 1 HMO

For p ≥ 2:

E[|Pt f |p] ≤ E[|Pt f |2] ≤ e−2tkE[f 2] = e−2tkE[|f |p].

For 1 < p < 2 by (1/(2− p), 1/(p − 1)) Holder inequality

E[|Pt f |p] = E[|Pt f |2−p|Pt f |2p−2|] ≤ E[|Pt f |]2−pE[|Pt f |2]p−1

≤ E[|f |]2−pe−2tk(p−1)E [|f |2]p−1 = e−2tk(p−1)E[|f |p].



An Easy Proof

For f ∈ L≥k :


For p ≥ 2:







An Easy Proof

For f ∈ L≥k :


For p ≥ 2:







Contraction in the first k = 1 tail space

For f ∈ L≥1(−1, 1n) (E[f ] = 0):

‖Pt f ‖p ≤ e−c(p)min(t,t2)‖f ‖p, p ≥ 2, Meyer, Mendel-Naor

‖Pt f ‖p ≤ e−c(p)t‖f ‖p, p > 1, Conj: Mendel-Naor

‖Pt f ‖p ≤ e−c(p)tM(n)e−δ(n)t‖f ‖p, p > 1, Hino

‖Pt f ‖p ≤ e−c(p)t‖f ‖p, p > 1, HMO



A harder proof

For f ∈ L≥1(−1, 1n) (E[f ] = 0):


Proof based a new type of Poincare inequality when E[f ] = 0:

E[|f |p−1sgn(f )Lf ] ≥ r(p)E [|f |p], r(p) :=2p − 2

p2 − 2p + 2p > 1.

So ddt exp(r(p)tE[|Pt |p]) is:

exp(r(p)t)(r(p)E[|Pt f |p]− p|Pt f |p−1sgn(Pt f )LPt f

)≤ 0.



A harder proof

For f ∈ L≥1(−1, 1n) (E[f ] = 0):


Proof based a new type of Poincare inequality when E[f ] = 0:

E[|f |p−1sgn(f )Lf ] ≥ r(p)E [|f |p], r(p) :=2p − 2

p2 − 2p + 2p > 1.

So ddt exp(r(p)tE[|Pt |p]) is:

exp(r(p)t)(r(p)E[|Pt f |p]− p|Pt f |p−1sgn(Pt f )LPt f

)≤ 0.



Isoperimetry in Tail Spaces

Let L>k+ := f : f (S) = 0, ∀0 < |S | ≤ k.

Harper’s isoperimetric inequality: For f : −1, 1n → 0, 1:

n∑i=1

Ii (f ) ≥ 2

log 2E[f ] log(1/E[f ]).

Note:∑n

i=1 Ii (f ) =∑

S |S |f 2(S)

Kalai: Does there exist ω(k)→∞ such that

n∑i=1

Ii (f ) ≥ ω(k)E[f ] log(1/E[f ]) for f ∈ L>k+ .

HMO: No.




Let L>k+ := f : f (S) = 0, ∀0 < |S | ≤ k.


n∑i=1

Ii (f ) ≥ 2


Note:∑n

i=1 Ii (f ) =∑

S |S |f 2(S)


n∑i=1


HMO: No.




Let L>k+ := f : f (S) = 0, ∀0 < |S | ≤ k.


n∑i=1

Ii (f ) ≥ 2


Note:∑n

i=1 Ii (f ) =∑

S |S |f 2(S)


n∑i=1


HMO: No.




Let L>k+ := f : f (S) = 0, ∀0 < |S | ≤ k.


n∑i=1

Ii (f ) ≥ 2


Note:∑n

i=1 Ii (f ) =∑

S |S |f 2(S)


n∑i=1


HMO: No.




Let L>k+ := f : f (S) = 0, ∀0 < |S | ≤ k.


n∑i=1

Ii (f ) ≥ 2


Note:∑n

i=1 Ii (f ) =∑

S |S |f 2(S)


n∑i=1


HMO: No.



KKL in Tail Spaces

KKL Thm: For f : 0, 1n → 0, 1 it holds thatmax Ii (f ) ≥ cVar [f ] log n/n.

Recall:∑n

i=1 Ii (f ) =∑

S |S |f 2(S).

Kalai, Hatami: Does there exist ω(k)→∞ such that

max Ii (f ) ≥ ω(k)Var [f ] log n

nfor f ∈ L>k .

HMO: No.



KKL in Tail Spaces


Recall:∑n

i=1 Ii (f ) =∑

S |S |f 2(S).



nfor f ∈ L>k .

HMO: No.



KKL in Tail Spaces


Recall:∑n

i=1 Ii (f ) =∑

S |S |f 2(S).



nfor f ∈ L>k .

HMO: No.



KKL in Tail Spaces


Recall:∑n

i=1 Ii (f ) =∑

S |S |f 2(S).



nfor f ∈ L>k .

HMO: No.



KKL in Tail Spaces


Recall:∑n

i=1 Ii (f ) =∑

S |S |f 2(S).



nfor f ∈ L>k .

HMO: No.



On codes and tails

Work with 0, 1n = F n2 . Given a linear code C ⊆ F n

2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C

MacWilliams identities: C ∈ L>k+ iff w(C+) > k .

Claim: there exists a γ > 1 and functionsg : 0, 1γm → 0, 1 with g ∈ L>m

+ and2−3m ≤ P[g = 1] ≤ 2−m.

Pf: Let g be a dual of a good code.

Note that

γm∑i=1

Ii (g) ≤ γmP[g = 1] ≤ γE[g ] log(1/E[g ]).

Tight for Harper’s inequality up to constants.



On codes and tails


2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C



+ and2−3m ≤ P[g = 1] ≤ 2−m.


Note that

γm∑i=1





On codes and tails


2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C



+ and2−3m ≤ P[g = 1] ≤ 2−m.


Note that

γm∑i=1





On codes and tails


2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C



+ and2−3m ≤ P[g = 1] ≤ 2−m.


Note that

γm∑i=1





On codes and tails


2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C



+ and2−3m ≤ P[g = 1] ≤ 2−m.


Note that

γm∑i=1





On codes and tails


2 , letw(C ) := min‖x‖1 : 0 6= x ∈ C and

C+ := y ∈ F n2 : ⊕n

i=1yixi = 0, ∀x ∈ C



+ and2−3m ≤ P[g = 1] ≤ 2−m.


Note that

γm∑i=1





The Coding Tribes Function

KKL Thm: For f : −1, 1n → −1, 1 it holds thatmax Ii (f ) ≥ cVar [f ] log n/n.

Ben-Or and Linial Tribes f are tight for the Thm.

f (x) = g(x1, . . . , xr )∨. . .∨g(x(b−1)r+1, . . . , xbr ), br = n, r = Θ(log n)

g = ANDlog2 n−log log n =⇒ E[g ] = Θ(log n/n) =⇒ Var(f ) = Θ(1).

Then: max Ii (f ) ≤ O(P[g = 1]) ≤ O(log n/n).

HMO: g := dual of a good code on O(log n) bits withE[g ] = Θ(log n/n).

g ∈ Lk=c log n+ =⇒ f ∈ Lk=c log n

+ by writing Fourierexpression.

Tight for KKL.

More work - make the function balanced.












Tight for KKL.













Tight for KKL.













Tight for KKL.













Tight for KKL.













Tight for KKL.













Tight for KKL.













Tight for KKL.









Questions??








Questions??


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Correlation Distillation