# Estimating Transitive Closure via Sampling

In this post, I describe an algorithm of Edith Cohen, which estimates the size of the transitive closure of a given directed graph in near-linear time. This simple, but extremely clever algorithm uses ideas somewhat similar to the algorithm of Flajolet–Martin for estimating the number of distinct elements in a stream, and to MinHash sketch of Broder1.

Suppose we have a large directed graph with $n$ vertices and $m$ directed edges. For a vertex $v$, let us denote $R_v$ the set of vertices that are reachable from $v$. There are two known ways to compute sets $R_v$ (all at the same time):

• Perform Depth-First Search (DFS) from each vertex. This takes time $O(nm)$, which is the best known bound for sparse graphs;
• Use fast matrix multiplication, which takes time $O(n^{2.37\ldots})$. This algorithm is better for dense graphs.

Can we do better? Turns out we can, if we are OK with merely approximating the size of every $R_v$. Namely, the following theorem was proved back in 1994:

Theorem 1. There exists a randomized algorithm for computing $(1 + \varepsilon)$-multiplicative approximation for every $|R_v|$ with running time $\varepsilon^{-2}\cdot m \cdot \mathrm{poly}(\log n)$.

Instead of spelling out the full proof, I will present it as a sequence of problems: each of them will likely be doable for a mathematically mature reader. Going through the problems should be fun, and besides, it will save me some typing.

Problem 1. Let $f \colon V \to [0, 1]$ be a function that assigns random independent and uniform reals between 0 and 1 to every vertex. Let us define $g(v) = \min_{w \in R_v} f(w)$. Show how to compute values of $g(v)$ for all vertices $v$ at once in time $m \cdot \mathrm{poly}(\log n)$.

Problem 2. For a positive integer $k$, denote $U_k$ the distribution of the minimum of $k$ independent and uniform reals between 0 and 1. Suppose we receive several independent samples from $U_k$ with an unknown value of $k$. Show that we can obtain a $(1 + \varepsilon)$-multiplicative approximation of $k$ with probability $1 - \delta$ using as few as $O(\log(1 / \delta) / \varepsilon^2)$ samples.

Problem 3. Combine the solutions for two previous problems and prove Theorem 1.

### Footnotes

1. These similarities explain my extreme enthusiasm towards the algorithm. Sketching-based techniques are useful for a problem covered in 6.006, yay!

Ilya

# Better Circuit Lower Bounds for Explicit Functions

Below is a post by Ilya on a new circuit lower bound.

Let $f \colon \{0, 1\}^n \to \{0, 1\}$ be a Boolean function of $n$ arguments. What is the smallest circuit that can compute $f$ if the set of allowed gates consists of all the unary and binary functions? The size of a circuit is simply the number of gates in it.

It’s easy to show that a random function requires circuits of size $\Omega(2^n / n)$ (this is tight in the worst case), but a big question in computational complexity is to provide a simple enough function that requires large enough circuits to compute.

Basically, if one comes up with a function that lies in $\mathrm{NP}$ and requires circuits of size super-polynomial in $n$, then $\mathrm{P} \ne \mathrm{NP}$, and one of the Millenium Problems is solved!

How far are we from accomplishing this? Well, until recently, the best lower bound for a function from NP was $3n$ [Blum 1984] (remember that eventually we are aiming at super-polynomial lower bounds!).

I’m very excited to report that very recently a better lower bound has been proved by Magnus Gausdal Find, Sasha Golovnev, Edward Hirsch and Sasha Kulikov! Is it super-polynomial in $n$? Is it at least super-linear in $n$?

Well, not really. The new lower bound is $3.011n$! You may ask: why one should be excited about such a seemingly weak improvement (which, nevertheless, took the authors several years of hard work, as far as I know)?

One reason is that this is the first progress on one of the central questions in computational complexity in more than 30 years. In general, researchers like to invent new models and problems and tend to forget good old “core” questions very easily. I’m happy to see this not being the case here.

Besides, the new result is, in fact, much more general than say the Blum’s. The authors prove that any function that is not constant on every sufficiently high-dimensional affine subspace requires large circuits. Then, they simply invoke the recent result of Ben-Sasson and Kopparty, who construct an explicit function with this property. That is, the paper shows a certain pseudo-randomness property to be enough for better lower bounds. Hopefully, there will be more developments in this direction.

Is this result one more step towards proving $\mathrm{P} \ne \mathrm{NP}$? Time will tell.

# Purifying spoiled randomness with spoiled randomness

Recently a preprint was posted to ECCC, with the pulse-quickening title “Explicit Two-Source Extractors and Resilient Functions“. If the result is correct, then it really is — shall I say it — a breakthrough in theoretical computer science. One of my favorite things about TCS is how fresh it is. It seems like every other week, an exciting result in all sorts of subareas are announced on arxiv or ECCC. I thought I’d use this as an opportunity to (briefly) explain what this breakthrough is about, for those who aren’t theoretical computer scientists.

Computer programs need sequences of random numbers all the time. Encryption, games, scientific applications need it. But these applications usually assume that they have access to completely unpredictable random number sequences that have no internal patterns or regularities.

But where in the world would we find such perfect, pristine sources of random numbers? The methods that people devise to generate sources of randomness is a fascinating subject in its own right, but long story short, it’s actually really, really hard to get our hands on such good random numbers. Pure randomness, as it turns out, is a valuable resource that is as rare as — perhaps even rarer than — gold or diamond. Continue reading

# Distribution Testing: Do It With Class!

After a long break since the last time we touched upon distribution testing, we are back to discuss two papers that appeared this month on the arXiv — and hopefully some of their implications.

• [ADK15] Optimal testing for properties of distributions.
• [CDRG15] Testing Shape Restrictions of Discrete Distributions.

# Sublinear Day at MIT

On Friday, April 10th, MIT will be hosting the second Sublinear Algorithms Day. This event will bring together researchers in the northeast for a day of interaction and discussion.

Sublinear Day will feature talks by five experts in the areas of sublinear and streaming algorithms: Costis Daskalakis, Robert Krauthgamer, Jelani Nelson, Shubhangi Saraf, and Paul Valiant — each giving a 45-minute presentation on the hot and latest developments in their fields.

Additionally, for the first time this year, we will have a poster session! We strongly encourage young researchers (particularly students and postdocs) to present work related to sublinear algorithms. Abstract submission details are available here.

So what are you waiting for? Registration is available here, and we hope to see you at the event!

Website: http://tinyurl.com/sublinearday2
Poster: http://tinyurl.com/sublinearday2poster
Contact g@csail.mit.edu with any questions.

Gautam Kamath

# Sketching and Embedding are Equivalent for Norms

## Summary

In this post I will show that any normed space that allows good sketches is necessarily embeddable into an $\ell_p$ space with $p$ close to $1$. This provides a partial converse to a result of Piotr Indyk, who showed how to sketch metrics that embed into $\ell_p$ for $0 < p \le 2$. A cool bonus of this result is that it gives a new technique for obtaining sketching lower bounds.

This result appeared in a recent paper of mine that is a joint work with Alexandr Andoni and Robert Krauthgamer. I am pleased to report that it has been accepted to STOC 2015.

## Sketching

One of the exciting relatively recent paradigms in algorithms is that of sketching. The high-level idea is as follows: if we are interested in working with a massive object $x$, let us start with compressing it to a short sketch $\mathrm{sketch}(x)$ that preserves properties of $x$ we care about. One great example of sketching is the Johnson-Lindenstrauss lemma: if we work with $n$ high-dimensional vectors and are interested in Euclidean distances between them, we can project the vectors on a random $O(\varepsilon^{-2} \cdot \log n)$-dimensional subspace, and this will preserve with high probability all the pairwise distances up to a factor of $1 + \varepsilon$.

It would be great to understand, for which computational problems sketching is possible, and how efficient it can be made. There are quite a few nice results (both upper and lower bounds) along these lines (see, e.g., graph sketching or a recent book about sketching for numerical linear algebra), but the general understanding has yet to emerge.

## Sketching for metrics

One of the main motivations to study sketching is fast computation and indexing of similarity measures $\mathrm{sim}(x, y)$ between two objects $x$ and $y$. Often times similarity between objects is modeled by some metric $d(x, y)$ (but not always! think KL divergence): for instance the above example of the Euclidean distance falls into this category. Thus, instantiating the above general question one can ask: for which metric spaces there exist good sketches? That is, when is it possible to compute a short sketch $\mathrm{sketch}(x)$ of a point $x$ such that, given two sketches $\mathrm{sketch}(x)$ and $\mathrm{sketch}(y)$, one is able to estimate the distance $d(x, y)$?

The following communication game captures the question of sketching metrics. Alice and Bob each have a point from a metric space $X$ (say, $x$ and $y$, respectively). Suppose, in addition, that either $d_X(x, y) \le r$ or $d_X(x, y) > D \cdot r$ (where $r$ and $D$ are the parameters known from the beginning). Both Alice and Bob send messages $\mathrm{sketch}(x)$ and $\mathrm{sketch}(y)$ that are $s$ bits long to Charlie, who is supposed to distinguish two cases (whether $d_X(x, y)$ is small or large) with probability at least $0.99$. We assume that all three parties are allowed to use shared randomness. Our main goal is to understand the trade-off between $D$ (approximation) and $s$ (sketch size).

Arguably, the most important metric spaces are $\ell_p$ spaces. Formally, for $1 \leq p \leq \infty$ we define $\ell_p^d$ to be a $d$-dimensional space equipped with distance

$\|x - y\|_p = \Bigl(\sum_{i=1}^d |x_i - y_i|^p\Bigr)^{1/p}$

(when $p = \infty$ this expression should be understood as $\max_{1 \leq i \leq d} |x_i - y_i|$). One can similarly define $\ell_p$ spaces for $0 < p < 1$; even if the triangle inequality does not hold for this case, it is nevertheless a meaningful notion of distance.

It turns out that $\ell_p$ spaces exhibit very interesting behavior, when it comes to sketching. Indyk showed that for $0 < p \le 2$ one can achieve approximation $D = 1 + \varepsilon$ and sketch size $s = O(1 / \varepsilon^2)$ for every $\varepsilon > 0$ (for $1 \le p \le 2$ this was established before by Kushilevitz, Ostrovsky and Rabani). It is quite remarkable that these bounds do not depend on the dimension of a space. On the other hand, for $\ell_p^d$ with $p > 2$ the dependence on the dimension is necessary. It turns out that for constant approximation $D = O(1)$ the optimal sketch size is $\widetilde{\Theta}(d^{1 - 2/p})$.

Are there any other examples of metrics that admit efficient sketches (say, with constant $D$ and $s$)? One simple observation is that if a metric embeds well into $\ell_p$ for $0 < p \le 2$, then one can sketch this metric well. Formally, we say that a map between metric spaces $f \colon X \to Y$ is an embedding with distortion $\widetilde{D}$, if

$d_X(x_1, x_2) \leq C \cdot d_Y\bigl(f(x_1), f(x_2)\bigr) \leq \widetilde{D} \cdot d_X(x_1, x_2)$

for every $x_1, x_2 \in X$ and for some $C > 0$. It is immediate to see that if a metric space $X$ embeds into $\ell_p$ for $0 < p \le 2$ with distortion $O(1)$, then one can sketch $X$ with $s = O(1)$ and $D = O(1)$. Thus, we know that any metric that embeds well into $\ell_p$ with $0 < p \le 2$ is efficiently sketchable. Are there any other examples? The amazing answer is that we don’t know!

## Our results

Our result shows that for a very important class of metrics—normed spaces—embedding into $\ell_p$ is the only possible way to obtain good sketches. Formally, if a normed space $X$ allows sketches of size $s$ for approximation $D$, then for every $\varepsilon > 0$ the space $X$ embeds into $\ell_{1 - \varepsilon}$ with distortion $O(sD / \varepsilon)$. This result together with the above upper bound by Indyk provides a complete characterization of normed spaces that admit good sketches.

Taking the above result in the contrapositive, we see that non-embeddability implies lower bounds for sketches. This is great, since it potentially allows us to employ many sophisticated non-embeddability results proved by geometers and functional analysts. Specifically, we prove two new lower bounds for sketches: for the planar Earth Mover’s Distance (building on a non-embeddability theorem by Naor and Schechtman) and for the trace norm (non-embeddability was proved by Pisier). In addition to it, we are able to unify certain known results: for instance, classify $\ell_p$ spaces and the cascaded norms in terms of “sketchability”.

## Overview of the proof

Let me outline the main steps of the proof of the implication “good sketches imply good embeddings”. The following definition is central to the proof. Let us call a map $f \colon X \to Y$ between two metric spaces $(s_1, s_2, \tau_1, \tau_2)$-threshold, if for every $x_1, x_2 \in X$:

• $d_X(x_1, x_2) \leq s_1$ implies $d_Y\bigl(f(x_1), f(x_2)\bigr) \leq \tau_1$,
• $d_X(x_1, x_2) \geq s_2$ implies $d_Y\bigl(f(x_1), f(x_2)\bigr) \geq \tau_2$.

One should think of threshold maps as very weak embeddings that merely
preserve certain distance scales.

The proof can be divided into two parts. First, we prove that for a normed space $X$ that allows sketches of size $s$ and approximation $D$ there exists a $(1, O(sD), 1, 10)$-threshold map to a Hilbert space. Then, we prove that the existence of such a map implies the existence of an embedding into $\ell_{1 - \varepsilon}$ with distortion $O(sD / \varepsilon)$.

The first half goes roughly as follows. Assume that there is no $(1, O(sD), 1, 10)$-threshold map from $X$ to a Hilbert space. Then, by convex duality, this implies certain Poincaré-type inequalities on $X$. This, in turn, implies sketching lower bounds for $\ell_{\infty}^k(X)$ (the direct sum of $k$ copies of $X$, where the norm is definied as the maximum of norms of the components) by a result of Andoni, Jayram and Pătrașcu (which is based on a very important notion of information complexity). Then, crucially using the fact that $X$ is a normed space, we conclude that $X$ itself does not have good sketches (this step follows from the fact that every normed space is of type $1$ and is of cotype $\infty$).

The second half uses tools from nonlinear functional analysis. First, building on an argument of Johnson and Randrianarivony, we show that for normed spaces $(1, O(sD), 1, 10)$-threshold map into a Hilbert space implies a uniform embedding into a Hilbert space—that is, a map $f \colon X \to H$, where $H$ is a Hilbert space such that

$L\bigl(\|x_1 - x_2\|_X\bigr) \leq \bigl\|f(x_1) - f(x_1)\bigr\|_H \leq U\bigl(\|x_1 - x_2\|_X\bigr),$

where $L, U \colon [0; \infty) \to [0; \infty)$ are non-decreasing functions such that $L(t) > 0$ for every $t > 0$ and $U(t) \to 0$ as $t \to 0$. Both $L$ and $U$ are allowed to depend only on $s$ and $D$. This step uses a certain Lipschitz extension-type theorem and averaging via bounded invariant means. Finally, we conclude the proof by applying theorems of Aharoni-Maurey-Mityagin and Nikishin and obtain a desired (linear) embedding of $X$ into $\ell_{1 - \varepsilon}$.

## Open problems

Let me finally state several open problems.

The first obvious open problem is to extend our result to as large class of general metric spaces as possible. Two notable examples one should keep in mind are the Khot-Vishnoi space and the Heisenberg group. In both cases, a space admits good sketches (since both spaces are embeddable into $\ell_2$-squared), but neither of them is embeddable into $\ell_1$. I do not know, if these spaces are embeddable into $\ell_{1 - \varepsilon}$, but I am inclined to suspect so.

The second open problem deals with linear sketches. For a normed space, one can require that a sketch is of the form $\mathrm{sketch}(x) = Ax$, where $A$ is a random matrix generated using shared randomness. Our result then can be interpreted as follows: any normed space that allows sketches of size $s$ and approximation $D$ allows a linear sketch with one linear measurement and approximation $O(sD)$ (this follows from the fact that for $\ell_{1 - \varepsilon}$ there are good linear sketches). But can we always construct a linear sketch of size $f(s)$ and approximation $g(D)$, where $f(\cdot)$ and $g(\cdot)$ are some (ideally, not too quickly growing) functions?

Finally, the third open problem is about spaces that allow essentially no non-trivial sketches. Can one characterize $d$-dimensional normed spaces, where any sketch for approximation $O(1)$ must have size $\Omega(d)$? The only example I can think of is a space that contains a subspace that is close to $\ell_{\infty}^{\Omega(d)}$. Is this the only case?

Ilya

# Insensitive Intersections of Halfspaces – STOC 2014 Recaps (Part 11)

In the eleventh and final installment of our STOC 2014 recaps, Jerry Li tells us about a spectacularly elegant result by Daniel Kane. It’s an example of what I like to call a “one-page wonder” — this a bit of a misnomer, since Kane’s paper is slightly more than five pages long, but the term refers to any beautiful paper which is (relatively) short and readable.

We hope you’ve enjoyed our coverage of this year’s STOC. We were able to write about a few of our favorite results, but there’s a wealth of other interesting papers that deserve your attention. I encourage you to peruse the proceedings and discover some favorites of your own.

The Monday afternoon sessions kicked off with Daniel Kane presenting his work on the average sensitivity of an intersection of halfspaces. Usually, FOCS/STOC talks can’t even begin to fit all the technical details from the paper, but unusually, Daniel’s talk included a complete proof of his result, without omitting any details. Amazingly, his result is very deep and general, so something incredible is clearly happening somewhere.

At the highest level, Daniel deals with the study of a certain class of Boolean functions. When we classically think about Boolean functions, we think of things such as CNFs, DNFs, decision trees, etc., which map into things like $\mathbb{F}_2, \{0, 1\},$ or $\{\mbox{True}, \mbox{False}\}$, but today, and often in the study of Boolean analysis, we will think of functions as mapping $\{-1, 1\}^n$ to $\{-1, 1\}$, which is roughly equivalent for many purposes (O’Donnell has a nice rule of thumb as when to use one convention or the other here). Given a function $f:\{-1, 1\}^n \to \{-1, 1\}$, we can define two important measures of sensitivity. The first is the average sensitivity (or, for those of you like me who grew up with O’Donnell’s book, the total influence) of the function, namely,

$\mathbb{AS}(f) = \mathbb{E}_{x \sim \{-1, 1\}^n} [| \{i: f(x^{i \to 1}) \neq f(x^{i \to -1}) \} |]$

where $x^{i \to a}$ is simply $x$ with its $i$th coordinate set to $a$. The second is the noise sensitivity of the function, which is defined similarly: for a parameter $\varepsilon \in (0, 1)$, it is the probability that if we sample $x$ uniformly at random from $\{-1, 1\}^n$, then independently flip each of its bits with probability $\varepsilon$, the value of $f$ at these two inputs is different. We denote this quantity $\mathbb{NS}_\varepsilon (f)$. When we generate a string $y$ from a string $x$ in this way we say they are $1 - 2\varepsilon$-correlated. The weird function of $\varepsilon$ in that expression is because often we equivalently think of $y$ being generated from $x$ by independently keeping each coordinate of $x$ fixed with probability $1 - 2\varepsilon$, and uniformly rerandomizing that bit with probability $2 \varepsilon$.

Why are these two measures important? If we have a concept class of functions $\mathcal{F}$, then it turns out that bounds on these two quantities can often be translated directly into learning algorithms for these classes. By Fourier analytic arguments, good bounds on the noise sensitivity of a function immediately imply that the function has good Fourier concentration on low degree terms, which in turn imply that the so-called “low-degree algorithm” can efficiently learn the class of functions in the PAC model, with random access. Unfortunately, I can’t really give any more detail here without a lot more technical detail, see [2] for a good introduction to the topic.

Now why is the average sensitivity of a Boolean function important? First of all, trust me when I say that it’s a fundamental measure of the robustness of the function. If we identify $f$ with $f^{-1} (1)$, then the average sensitivity is how many edges cross from one subset into another (over $2^n$), so it is fundamentally related to the surface area of subsets of the hypercube, which comes up all over the place in Boolean analysis. Secondly, in some cases, we can translate between one measure and the other by considering restrictions of functions. To the best of my knowledge, this appears to be a technique first introduced by Peres in 1999, though his paper is from 2004 [3]. Let $f: \{-1, +1\}^n \to \mathbb{R}$. We wish to bound the noise sensitivity of $f$, so we need to see how it behaves when we generate $x$ uniformly at random, then $y$ as $1 - 2 \varepsilon$-correlated to $x$. Suppose $\varepsilon = 1/m$ for some integer $m$ (if not, just round it). Fix a partition of the $n$ coordinates into $m$ bins $B_1, \ldots, B_m$, and a $z \in \{-1, 1\}^n$. Then, for any string $s \in \{-1, 1\}^m$, we associate it with the string $x \in \{-1, 1\}^n$ whose $i$th coordinate is the $i$th coordinate of $z$ times the $j$th coordinate of $s$, if $i \in B_j$. Why are we doing this? Well, after some thought, it’s not too hard to convince yourself that if we choose the $m$ bins and the strings $z, s$ uniformly at random, then we get a uniformly random string $x$. Moreover, to generate a string $y$ which is $1 - 2 \varepsilon$-correlated with $x$, it suffices to, after having already randomly chosen the bins, $z$, and $s$, to randomly pick a coordinate of $s$ and flip its sign to produce a new string $s'$, and produce a new string $y$ with these choices of the bins, $z,$ and $s'$. Thus, importantly, we can reduce the process of producing $1 - 2 \varepsilon$-correlated strings to the process of randomly flipping one bit of some weird new function–but this is the process we consider when we consider the average sensitivity! Thus noise sensitivity of $f$ is exactly equal to the expected (over the random choice of the bins and $z$) average sensitivity of this weird restricted thing. Why this is useful will (hopefully) become clear later.

Since the title of the paper includes the phrase “intersection of halfspaces,” at some point I should probably define what an intersection of halfspaces is. First of all, a halfspace (or linear threshold function) is a Boolean function of the form $f(x) = \text{sgn}\left(w \cdot x - \theta\right)$ where $w \in \mathbb{R}^n, \theta \in \mathbb{R}$ and for concreteness let’s say $\text{sgn}\left(0\right) = 1$ (however, it’s not too hard to see that any halfspace has a representation so that the linear function inside the sign is never zero on the hypercube). Intuitively, take the hyperplane in $\mathbb{R}^n$ with normal vector $w$, then assign to all points which are in the same side as $w$ of the hyper plane the value $+1$, and the rest $-1$. Halfspaces are an incredibly rich family of Boolean functions which include arguably some of the important objects in Boolean analysis, such as the dictator functions, the majority function, etc. There is basically a mountain of work on halfspaces, due to their importance in learning theory, and as elementary objects which capture a surprising amount of structure.

Secondly, the intersection of $k$ functions $f_1, \ldots, f_k$ is the function which is $1$ at $x$ if and only $f_i (x) = 1$ for all $i$, and $-1$ otherwise. If we think of each $f_i$ as a $\{ \mbox{True}, \mbox{False}\}$ predicate on the boolean cube, then their intersection is simply their AND (or NOR, depending on your map from $\{-1, 1\}$ to $\{ \mbox{True}, \mbox{False}\}$).

Putting these together gives us the family of functions that Daniel’s work concerns. I don’t know what else to say other than they are a natural algebraic generalization of halfspaces. Hopefully you think these functions are interesting, but even if you don’t, it’s (kinda) okay, because, amazingly, it turns out Kane’s main result barely uses any properties of halfspaces! In fact, it only uses the fact that halfspaces are unate, that is, they are either monotone increasing or decreasing in each coordinate. In fact, he proves the following, incredibly general, theorem:

Theorem. [Kane14]
Let $f:\{-1, 1\}^n \to \{-1, 1\}$ be an intersection of $k$ unate functions. Then

$\mathbb{AS}(f) = O(\sqrt{n \log k})$.

I’m not going to go into too much detail about the proof; unfortunately, despite my best efforts there’s not much intuition I can compress out of it (in his talk, Daniel himself admitted that there was a lemma which was mysterious even to him). Plus it’s only roughly two pages of elementary (but extremely deep) calculations, just read it yourself! At a very, very high level, the idea is that intersecting a intersection of $k - 1$ halfspaces with one more can only increase the average sensitivity by a small factor.

The really attentive reader might have also figured out why I gave that strange reduction between noise sensitivity and average sensitivity. This is because, importantly, when we apply this weird process of randomly choosing bins to an intersection of halfspaces, the resulting function is still an intersection of halfspaces, just over fewer coordinates (besides their unate-ness, this is the only property of intersections of halfspaces that Daniel uses). Thus, since we now know how to bound the average sensitivity of halfspaces, we also know tight bounds for the noise sensitivities of intersection of halfspaces, namely, the following:

Theorem. [Kane14]
Let $f:\{-1, 1\}^n \to \{-1, 1\}$ be an intersection of $k$ halfspaces. Then

$\mathbb{NS}_\varepsilon (f) = O(\sqrt{\varepsilon \log k})$.

Finally, this gives us good learning algorithms for intersections of halfspaces.

The paper is remarkable; there had been previous work by Nazarov (see [4]) proving optimal bounds for sensitivities of intersections of halfspaces in the Gaussian setting, which is a more restricted setting than the Boolean setting (intuitively because we can simulate Gaussians by sums of independent Boolean variables), and there were some results in the Boolean setting, but they were fairly suboptimal [5]. Furthermore, all these proofs were scary: they were incredibly involved, used powerful machinery from real analysis, drew heavily on the properties of halfspaces, etc. On the other hand, Daniel’s proof of his main result (which I would say builds on past work in the area, except it doesn’t use anything besides elementary facts), well, I think Erdos would say this proof is from “the Book”.

[1] The Average Sensitivity of an Intersection of Half Spaces, Kane, 2014.
[2] Analysis of Boolean Functions, O’Donnell, 2014.
[3] Noise Stability of Weighted Majority, Peres, 2004.
[4] On the maximal perimeter of a convex set in $\mathbb{R}^n$ with respect to a Gaussian measure, Nazarov, 2003.
[5] An Invariance Principle for Polytopes, Harsha, Klivans, Meka, 2010.