**On closeness to \(k\)-wise uniformity**, by Ryan O’Donnell and Yu Zhao (arXiv)

In this paper, the authors consider the following structural question about probability distributions over the Boolean hypercube \(\{-1,1\}^n\): ” what is the relation between total variation distance \(\delta\) to \(k\)-wise independence, and bound \(\varepsilon\) on the Fourier coefficients of the distribution on degrees up to \(k\)?”

While this question might seem a bit esoteric at first glance, it has direct and natural applications to derandomization, and of course to distribution testing (namely, to test \(k\)-wise independence and its generalization, \((\varepsilon, k)\)-wise independence of distributions over the hypercube).

The main contribution here is to improve (by a \((\log n)^{O(k)}\) factor) the bounds on \(\delta (n,k,\varepsilon)\) over the previous work by Alon et al. [AAK+07], making them either tight (for \(k\) even) or near-tight. To do so, the authors introduce a new hammer to the game, using linear programming duality in the proof of both their upper and lower bounds.

**Property Testing for Differential Privacy**, by Anna Gilbert and Audra McMillan (arXiv)

Differential privacy, as introduced by Dwork et al., needs no introduction. Property testing, especially on this website, needs even less. *What about a combination of the two?* Namely, given black-box access to an algorithm claiming to perform a differentially private computation, how to test whether this is indeed the case?

Introducing and considering this quite natural question for the first time, this work shows, roughly speaking, that testing differential privacy is *hard*. Specifically, they show that for many notions of differential privacy (pure, approximate, and their distributional counterparts), testing is either impossible or possible but not with a sublinear number of queries (even when the tester is provided with side information about the black-box). In other terms, as the authors put it: trusting the privacy of an algorithm “requires compromise by either the verifier or algorithm owner” (and, in the latter case, even then it’s not a simple matter).

**Is your data low-dimensional?**, by Anindya De, Elchanan Mossel, and Joe Neeman (arXiv)

*(Well, is it?)* To state it upfront, I am biased here, as it is a problem I was very eager to see investigated to begin with. To recap, the question is as follows: “given query access to some unknown Boolean-valued function \(f\colon \mathbb{R}^n \to \{-1,1\}\) over the high-dimensional space \(\mathbb{R}^n\) endowed with the Gaussian measure, how can one check whether \(f\) only depends on “few” (i.e., \(k \ll n\)) variables?”

This is the continuous, Gaussian version of the (quite famous) junta testing problem, which has gathered significant attention over the past years *(the Gaussian version has, to the best of my knowledge, never been investigated).* Now, the above formulation has a major flaw: specifically, it is uninteresting. In Gaussian space*, who cares about the particular basis I expressed my input vector in? So a more relevant question, and that that the authors tackle, is the more robust and natural one: “given query access to some unknown Boolean-valued function \(f\colon \mathbb{R}^n \to \{-1,1\}\) over the high-dimensional space \(\mathbb{R}^n\) endowed with the Gaussian measure, how can one check whether \(f\) only depends on a low-dimensional linear combination of the variables?” Or, put differently, does all the relevant information for \(f\) live in a low-dimensional subspace?

De, Mossel, and Neeman show how can do this, non-adaptively, with a query complexity independent of the dimension \(n\) (hurray!), but instead polynomial in \(k\), the distance parameter \(\varepsilon\), and the * surface area \(s\)* of \(f\). And since this last parameter may seem quite arbitrary, they also proceed to show that a polynomial dependence in this \(s\) is indeed required.

**”In Gaussian space, no one can hear you change basis?”*

**Lower Bounds for Tolerant Junta and Unateness Testing via Rejection Sampling of Graphs**, by Amit Levi and Erik Waingarten (ECCC). This paper proves a number of new lower bounds for tolerant testing of Boolean functions, including non-adaptive \(k\)-junta testing and adaptive and non-adaptive unateness testing. Combined with upper bounds for these and related problems, these results establishes separation between the complexity of tolerant and non-tolerant testing for natural properties of Boolean functions, which have so far been elusive. As a technical tool, the authors introduce a new model for testing graph properties, termed the *rejection sampling model*. In this model, the algorithm queries a subset \(L\) of the vertices, and the oracle will sample an edge uniformly at random and output the intersection of the edge endpoints with the query set \(L\). The cost of an algorithm is measured as the sum of the query sizes. In order to prove the above lower bounds (in the standard model), they show a non-adaptive lower bound for testing bipartiteness (in their new model).

**Hierarchy Theorems for Testing Properties in Size-Oblivious Query Complexity**, by Oded Goldreich (ECCC). This work proves a hierarchy theorem for properties which are independent of the size of the object, and depend only on the proximity parameter \(\varepsilon\). Roughly, for essentially every function \(q : (0,1] \rightarrow \mathbb{N}\), there exists a property for which the query complexity is \(\Theta(q(\varepsilon))\). Such results are proven for Boolean functions, dense graphs, and bounded-degree graphs. This complements hierarchy theorems by Goldreich, Krivelevich, Newman, and Rozenberg, which give a hierarchy which depends on the object size.

**Finding forbidden minors in sublinear time: a \(O(n^{1/2+o(1)})\)-query one-sided tester for minor closed properties on bounded degree graphs**, by Akash Kumar, C. Seshadhri, and Andrew Stolman (ECCC). At the core of this paper is a sublinear algorithm for the following problem: given a graph which is \(\varepsilon\)-far from being \(H\)-minor free, find an \(H\)-minor in the graph. The authors provide a (roughly) \(O(\sqrt{n})\) time algorithm for such a task. As a concrete example, given a graph which is far from being planar, one can efficiently find an instance of a \(K_{3,3}\) or \(K_5\) minor. Using the graph minor theorem, this implies analogous results for any minor-closed property, nearly resolving a conjecture of Benjamini, Schramm and Shapira.

**Learning and Testing Causal Models with Interventions**, by Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, and Saravanan Kandasamy (arXiv). This paper considers the problem of learning and testing on causal Bayesian networks. Bayesian networks are a type of graphical model defined on a DAG, where each node has a distribution defined based on the value of its parents. A causal Bayesian network further allows “interventions,” where one may set nodes to have certain values. This paper gives efficient algorithms for learning and testing the distribution of these models, with \(O(\log n)\) interventions and \(\tilde O(n/\varepsilon^2)\) samples per intervention

**Property Testing of Planarity in the CONGEST model**, by Reut Levi, Moti Medina, and Dana Ron (arXiv). It is known that, in the CONGEST model of distributed computation, deciding whether a graph is planar requires a linear number of rounds. This paper considers the natural property testing relaxation, where we wish to determine whether a graph is planar, or \(\varepsilon\)-far from being planar. The authors show that this relaxation allows one to bypass this linear lower bound, obtaining a \(O(\log n \cdot \mathrm{poly(1/\varepsilon))}\) algorithm, complemented by an \(\Omega(\log n)\) lower bound.

**Flexible models for testing graph properties**, by Oded Goldreich (ECCC). Usually when testing graph properties, we assume that the vertex set is \([n]\), implying that we can randomly sample nodes from the graph. However, this assumes that the tester knows the value of \(n\), the number of nodes. This note suggests more “flexible” models, in which the number of nodes may be unknown, and we are only given random sampling access. While possible definitions are suggested, this note contains few results, leaving the area ripe for investigation of the power of these models.

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**Testing Linearity against Non-Signaling Strategies**, by Alessandro Chiesa, Peter Manohar, and Igor Shinkar (ECCC). This paper gives a new model for property testing, through the notion of non-signaling strategies. The exact definitions are quite subtle, but here’s a condensed view. For \(S \subseteq \{0,1\}^n\), let an \(S\)-partial function be one that is only defined on \(S\). Fix a “consistency” parameter \(k\). Think of the “input” as a collection of distributions, \(\{\mathcal{F}_S\}\), where each \(|S| \leq k\) and \(\mathcal{F}_S\) is a distribution of \(S\)-partial functions. We have a local consistency requirement: \(\{\mathcal{F}_S\}\) and \(\{\mathcal{F}_T\}\) must agree (as distributions) on restrictions to \(S \cap T\). In some sense, if we only view *pairs* of these distributions of partial functions, it appears as if they come from a single distributions of total functions. Let us focus on the classic linearity tester of Blum-Luby-Rubinfeld in this setting. We pick random \(x, y, x+y \in \{0,1\}^n\) as before, and query these values on a function \(f \sim {\mathcal{F}_{x,y,x+y}}\). The main question addressed is what one can say about \(\{\mathcal{F}_S\}\), if this linearity test passes with high probability. Intuitively (but technically incorrect), the main result is that \(\{\mathcal{F}_S\}\) is approximated by a “quasi-distribution” of linear functions.

**An Exponential Separation Between MA and AM Proofs of Proximity**, by Tom Gur, Yang P. Liu, and Ron D. Rothblum (ECCC). This result follows a line of work on understanding sublinear algorithms in proof systems. Think of the standard property testing setting. There is a property \(\mathcal{P}\) of \(n\)-bit strings, an input \(x \in \{0,1\}^n\), and a proximity parameter \(\epsilon > 0\). We add a proof \(\Pi\) that the tester (or the verifier) is allowed to use, and we define soundness and completeness in the usual sense of Arthur-Merlin protocols. For a \(\mathbb{MA}\)-proof of proximity, the proof \(\Pi\) can only depend on the string \(x\). In a \(\mathbb{AM}\)-proof of proximity, the proof can additionally depend on the random coins of the tester (which determine the indices of \(x\) queried). Classic complexity results can be used to show that the latter subsume the former, and this paper gives a strong separation. Namely, there is a property \(\mathcal{P}\) where any \(\mathbb{MA}\)-proof of proximity protocol (or tester) requires \(\Omega(n^{1/4})\)-queries of the input \(x\), but there exists an \(\mathbb{AM}\)-proof of proximity protocol making \(O(\log n)\) queries. Moreover, this property is quite natural; it is simply the encoding of permutations.

**Testing Identity of Multidimensional Histograms**, by Ilias Diakonikolas, Daniel M. Kane, and John Peebles (arXiv). A distribution over \([0,1]^d\) is a \(k\)-histogram if the domain can be partitioned into \(k\) axis-aligned cuboids where the probability density function is constant. Recent results show that such histograms can be learned in \(k \log^{O(d)}k\) samples (ignoring dependencies on accuracy/error parameters). Can we do any better for identity testing? This paper gives an affirmative answer. Given a known \(k\)-histogram \(p\), one can test (in \(\ell_1\)-distance) whether an unknown \(k\)-histogram \(q\) is equal to \(p\) in (essentially) \(\sqrt{k} \log^{O(d)}(dk)\) samples. There is a nearly matching lower bound, when \(k = \exp(d)\).

**Distributed Simulation and Distributed Inference**, by Jayadev Acharya, Clément L. Canonne, and Himanshu Tyagi (arXiv ECCC). This papers introduces a model of distributed simulation, which generalizes distribution testing and distributed density estimation. Consider some unknown distribution \(\mathcal{D}\) with support \([k]\), and a “referee” who wishes to generate a single sample from \(\mathcal{D}\) (alternately, she may wish to determine if \(\mathcal{D}\) has some desired property). The referee can communicate with “players”, each of whom can generate a single independent sample from \(\mathcal{D}\). The catch is that each player can communicate at most \(\ell\) < \(log_2k\) bits (otherwise, the player can simply communicate the sampled element). How many players are needed for the referee to generate a single sample? The paper first proves that this task is basically impossible with a (worst-case) finite number of players, but can be done with expected \(O(k/2^\ell)\) players (and this is optimal). This can plugged into standard distribution testing results, to get inference results in this distributed, low-communication setting. For example, the paper shows that identity testing can be done with \(O(k^{3/2}/2^\ell)\) players.

**Edge correlations in random regular hypergraphs and applications to subgraph testing**, by Alberto Espuny Díaz, Felix Joos, Daniela Kühn, and Deryk Osthus (arXiv). While testing subgraph-freness in the dense graph model is now well-understood, after a series of works culminating in a complete characterization of the testing problems which admit constant-query testers, the corresponding question for hypergraphs is far from resolved. In this paper, the authors develop new techniques for the study of study of random regular hypergraphs, which imply new testing results for subhypergraph-freeness testing, improving on the state-of-the-art for some parameter regimes (e.g., when the input graph satisfies some average-degree condition).

Back from hypergraphs to graphs, we also have:

**The Subgraph Testing Model**, by Oded Goldreich and Dana Ron (ECCC). Here, the authors introduce a new model for property testing of graphs, where the goal is to test if an unknown *subgraph* \(F\) of an explicitly given graph \(G=(V,E)\) satisfies the desired property. The testing algorithm is provided access to \(F\) via membership queries, i.e., through query access to the indicator function \(\mathbf{1}_F\colon E \to \{0,1\}\). (In some *very* hazy sense, this is reminiscent of the active learning or testing frameworks, where one gets more or less free access to unlabeled data but pays to see their label.) As a sample of the results obtained, the paper establishes that this new model and the bounded-degree graph model are incomparable: there exist properties easier to test in one model than the other, and vice-versa — and some properties equally easy to test in both.

And finally, to drive home the point that “models matter a lot,” we have our third paper:

**Every set in P is strongly testable under a suitable encoding**, by Irit Dinur, Oded Goldreich, and Tom Gur (ECCC). It is known that the choice of representation of the objects has a large impact in property testing: for instance, the complexity of testing a given property can change drastically between the dense and bounded-degree graph models. This work provides another example of such a strong dependence on the representation: while membership to some sets in \(P\) is known to be hard to test, the authors here prove that, for every set \(S\in P\), there exists a (polynomial-time, invertible) encoding \(E_S\colon \{0,1\}^\ast\to \{0,1\}^\ast\) such that testing membership to \(S\) under this encoding is easy. (They actually show even stronger a statement: namely, that under this encoding the set admits a “proximity-oblivious tester,” that is a constant-query testing algorithm which rejects with probability function of the distance to \(S\).)

Finally, on a non-property testing note: Edith Cohen, Vitaly Feldman, Omer Reingold, and Ronitt Rubinfeld recently wrote a pledge for inclusiveness in the TCS community, available here: https://www.gopetition.com/petitions/a-pledge-for-inclusiveness-in-toc.html

If you haven’t seen it already, we encourage you to read it.

**Update:** Fixed a mistake in the overview of the second paper; as pointed out by Oded in the comments, the main comparison was between the new model and the bounded-degree graph model, not the dense graph one.

**Locally Private Hypothesis Testing**, by Or Sheffet (arXiv). We now have a very mature understanding of the sample complexity of distributional identity testing — given samples from a distribution \(p\), is it equal to, or far from, some model hypothesis \(q\)? Recently, several papers have studied this problem under the additional constraint of *differential privacy*. This paper strengthens the privacy constraint to *local* privacy, where each sample is locally noised before being provided to the testing algorithm.

**Distribution-free Junta Testing**, by Xi Chen, Zhengyang Liu, Rocco A. Servedio, Ying Sheng, and Jinyu Xie (arXiv). Testing whether a function is a \(k\)-junta is very well understood — when done with respect to the uniform distribution. In particular, the adaptive complexity of this problem is \(\tilde \Theta(k)\), while the non-adaptive complexity is \(\tilde \Theta(k^{3/2})\). This paper studies the more challenging task of distribution-free testing, where the distance between functions is measured with respect to some unknown distribution. The authors show that, while the adaptive complexity of this problem is still polynomial (at \(\tilde O(k^2)\)), the non-adaptive complexity becomes exponential: \(2^{\Omega(k/3)}\). In other words, there’s a qualitative gap between the adaptive and non-adaptive complexity, which does not appear when testing with respect to the uniform distribution.

**The Vertex Sample Complexity of Free Energy is Polynomial**, by Vishesh Jain, Frederic Koehler, and Elchanan Mossel (arXiv). This paper studies the classic question of estimating (the logarithm of) the partition function of a Markov Random Field, a highly-studied topic in theoretical computer science and statistical physics. As the title suggests, the authors show that the vertex sample complexity of this quantity is polynomial. In other words, randomly subsampling a \(\mathrm{poly}(1/\varepsilon)\)-size graph and computing its free energy gives a good approximation to the free energy of the overall graph. This is in contrast to more general graph properties, for the vertex sample complexity is super-exponential in \(1/\varepsilon\).

**Entropy Rate Estimation for Markov Chains with Large State Space**, by Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, and Tiancheng Yu (arXiv). Entropy estimation is now quite well-understood when one observes independent samples from a discrete distribution — we can get by with a barely-sublinear sample complexity, saving a logarithmic factor compared to the support size. This paper shows that these savings can also be enjoyed in the case where we observe a sample path of observations from a Markov chain.

**Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance**,** **by Yanjun Han, Jiantao Jiao, and Tsachy Weissman (arXiv). Speaking more generally of the above problem: there has been significant work into estimating symmetric properties of distributions, i.e., those which do not change when the distribution is permuted. One natural method for estimating such properties is to estimate the sorted distribution, then apply the plug-in estimator for the quantity of interest. The authors give an improved estimator for the sorted distribution, improving on the results of Valiant and Valiant.

**INSPECTRE: Privately Estimating the Unseen**, by Jayadev Acharya, Gautam Kamath, Ziteng Sun, and Huanyu Zhang (arXiv). One final work in this area — this paper studies the estimation of symmetric distribution properties (including entropy, support size, and support coverage), but this time while maintaining differentially privacy of the sample. By using estimators for these tasks with low sensitivity, one can additionally obtain privacy for a low or no additional cost over the non-private sample complexity.

**Adaptive Boolean Monotonicity Testing in Total Influence Time**, by Deeparnab Chakrabarty and C. Seshadhri (arXiv ECCC). The problem of testing monotonicity of Boolean functions \(f:\{0,1\}^n \to \{0,1\}\) has seen a lot of progress recently. After the breakthrough results of Khot-Minzer-Safra giving a \(\widetilde{O}(\sqrt{n})\) non-adaptive tester, Blais-Belovs proved the first polynomial lower bound for adaptive testers, recently improved to \(O(n^{1/3})\) by Chen, Waingarten, and Xi. The burning question: does adaptivity help? This result shows gives an adaptive tester that runs in \(O(\mathbf{I}(f))\), the total influence of \(f\). Thus, we can beat these lower bounds (and the non-adaptive complexity) for low influence functions.

**Adaptive Lower Bound for Testing Monotonicity on the Line**, by Aleksandrs Belovs (arXiv). More monotonicity testing! But this time on functions \(f:[n] \to [r]\). Classic results on property testing show that monotonicity can be tested in \(O(\varepsilon^{-1}\log n)\) time. A recent extension of these ideas by Pallavoor-Raskhodnikova-Varma replace the \(\log n\) with \(\log r\), an improvement for small ranges. This paper proves an almost matching lower bound of \((\log r)/(\log\log r)\). The main construction can be used to give a substantially simpler proof of an \(\Omega(d\log n)\) lower bound for monotonicity testing on hypergrids \(f:[n]^d \to \mathbb{N}\). The primary contribution is giving explicit lower bound constructions and avoiding Ramsey-theoretical arguments previously used for monotonicity lower bounds.

**Earthmover Resilience and Testing in Ordered Structures**, by Omri Ben-Eliezer and Eldar Fischer (arXiv). While there has been much progress on understanding the constant-time testability of graphs, the picture is not so clear for *ordered* structures (such as strings/matrices). There are a number of roadblocks (unlike the graph setting): there are no canonical testers for, say, string properties, there are testable properties that are not tolerant testable, and Szemeredi-type regular partitions may not exist for such properties. The main contribution of this paper is to find a natural, useful condition on ordered properties such that the above roadblocks disappear hold, and thus we have strong testability results. The paper introduces the notion of Earthmover Resilient properties (ER). Basically, a graph properties is a property of symmetric matrices that is invariant under permutation of base elements (rows/columns). An ER property is one that is invariant under mild perturbations of the base elements. The natural special cases of ER properties are those over strings and matrices, and it is includes all graph properties as well as image properties studied in this context. There are a number of characterization results. Most interestingly, for ER properties of images (binary matrices) and edge-colored ordered graphs, the following are equivalent: existence of canonical testers, tolerant testability, and regular reducibility.

**Nondeterminisic Sublinear Time Has Measure 0 in P**, by John Hitchcock and Adewale Sekoni (arXiv). Not your usual property testing paper, but on sublinear (non-deterministic) time nonetheless. Consider the complexity class of \(NTIME(n^\delta)\), for \(\delta < 1\). This paper shows that this complexity class is a "negligible" fraction of \(P\). (The analogous result was known for \(\alpha < 1/11\) by Cai-Sivakumar-Strauss.) This requires a technical concept of measure for languages and complexity classes. While I don’t claim to understand the details, the math boils down to understanding the following process. Consider some language \(\mathcal{L}\) and a martingale betting process that repeatedly tries to guess the membership of strings \(x_1, x_2, \ldots\) in a well-defined order. If one can define such a betting process with a limited computational resource that has unbounded gains, then \(\mathcal{L}\) has measure 0 with respect to that (limited) resource.

We begin with graphs:

**High Dimensional Expanders**, by Alexander Lubotzky (arXiv). This paper surveys the recent developments in studying high dimensional expander graphs, a recent generalization of expanders which has become quite active in the past years and has intimate connections to property testing.

**Generalized Turán problems for even cycles**, by Dániel Gerbner, Ervin Győri, Abhishek Methuku, and Máté Vizer (arXiv).

**A Generalized Turán Problem and its Applications**, by Lior Gishboliner and Asaf Shapira (arXiv).

In these two independent works, the authors study questions of a following flavor: two subgraphs (patterns) \(H,H’\), what is the maximum number of copies of \(H\) which can exist in a graph \(G\) promised to be \(H’\)-free? They consider the case where the said patterns are cycles on \(\ell,k\) vertices respectively, and obtain asymptotic bounds on the above quantity (the two papers obtain somewhat incomparable bounds, and the first focuses on the case where both \(\ell,k\) are even). These estimates, in turn, have applications to graph removal lemmata, as discussed in the second work (Section 1.2): specifically, it implies the existence of a removal lemma with a

tight super-polynomial bound, a question which was previously open.

**Approximating the Spectrum of a Graph**, by David Cohen-Steiner, Weihao Kong, Christian Sohler, and Gregory Valiant (arXiv). The authors obtain constant-time and query algorithm for the task of approximating (in \(\ell_1\) norm) the *spectrum* of a graph \(G\), i.e. the eigenvalues of its Laplacian, given random query access to the nodes of \(G\) and to the neighbors of any given node. They also study the applications of this result to property testing in the bounded-degree model, showing that a large class of spectral properties of high-girth graphs is testable.

Then, we go quantum:

**Schur-Weyl Duality for the Clifford Group with Applications: Property Testing, a Robust Hudson Theorem, and de Finetti Representations**, by David Gross, Sepehr Nezami, and Michael Walter (arXiv).

Introducing and studying a duality theory for the Clifford group, the authors are able (among other results) to resolve an open question in quantum property testing, establishing a constant-query tester (indeed, making 6 queries) for testing whether an unknown quantum state is a stabilizer state. The previous best upper bound was linear in the number of qubits, as it proceeded by learning the state (“testing by learning”).

**Quantum Lower Bound for a Tripartite Version of the Hidden Shift Problem**, by Aleksandrs Belovs (arXiv). This work introduces and studies a generalization of (both) the hidden shift and 3-sum problems, and shows an \(\Omega(n^{1/3})\) lower bound on its quantum query complexity. The author also considers a property testing version of the problem, for which he proves a similar lower bound—interestingly, this polynomial lower bound is shown using the adversary method, evading the “property testing barrier” which states that (a restricted version of) this method cannot yield better than a constant-query lower bound.

And to conclude, a distribution testing paper:

**Approximate Profile Maximum Likelihood**, by Dmitri S. Pavlichin, Jiantao Jiao, and Tsachy Weissman (arXiv) This paper proposes an efficient (linear-time) algorithm to approximate the profile maximum likelihood of a sequence of i.i.d. samples from an unknown distribution, i.e. the probability distribution which, ignoring the labels of the samples and keeping only the collision counts, maximizes the likelihood of the sequence observed. This provides a candidate solution to an open problem suggested by Orlitsky in a FOCS’17 workshop (see also Open problem 84), and one which would have direct implications to tolerant testing and estimation of symmetric properties of distributions.

**Agreement tests on graphs and hypergraphs,** by Irit Dinur, Yuval Filmus, and Prahladh Harsha (ECCC). This work looks at agreement tests and agreement theorems, which argue that if one checks if a number of local functions agree, then there exists a global function which agrees with most of them. This work extends previous work on direct product testing to local functions of higher degree, which corresponds to agreement tests on graphs and hypergraphs.

**Testing Conditional Independence of Discrete Distributions,** by Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart (arXiv). This paper focuses on testing whether a bivariate discrete distribution has independent marginals, conditioned on the value of a tertiary discrete random variable. More precisely, given realizations of \((X, Y, Z)\), test if \(X \perp Y \mid Z\). Unconditional independence testing (corresponding to the case when \(Z\) is constant) has been extensively studied by the community, with tight upper and lower bounds showing that the sample complexity has two regimes, depending on the tradeoff between the support size and the accuracy desired. This paper shows gives upper and lower bounds for this more general problem, showing a rich landscape depending on the relative value of the parameters.

A tad less than one year and a half ago, we announced on this blog a then-upcoming book on property testing, by Oded Goldreich. Now, just in time for the Holiday season, or for perusing on the beach if you live in the south hemisphere, the book is out!

Covering a broad swath of topics in property testing, *Introduction to Property Testing*, by Oded Goldreich (published by Cambridge University Press) is, quoting Amazon:

An extensive and authoritative introduction to property testing, the study of super-fast algorithms for the structural analysis of large quantities of data in order to determine global properties. This book can be used both as a reference book and a textbook, and includes numerous exercises.

See the book’s website for more resources and the (detailed) table of contents.

]]>**Proofs of Proximity for Distribution Testing**, by Alessandro Cheisa and Tom Gur (ECCC). Take 1 on distribution testing. As usual, we have a distribution \(\mathcal{D}\) over \([n]\), a property \(\mathbf{P}\) of distributions, and a proximity parameter \(\varepsilon > 0\). But our tester is now aided by a purported proof (or a prover). If \(\mathcal{D} \in \mathbf{P}\), there must exist a proof/prover strategy that makes the tester accept. If \(\mathcal{D}\) is \(\varepsilon\)-far from \(\mathbf{P}\), for any proof/prover strategy, the tester must reject with high probability. This paper studies a number of settings: deterministic vs randomized testers, proof (a la \(\mathbb{NP}\) or \(\mathbb{MA}\)) vs provers (a la \(\mathbb{IP}\)). There are a number of very intriguing results, so here are some highlights. For every property, there is a near-linear proof that allows for \(O(\sqrt{n})\) testers. For every property, either the proof length or the tester (sample) complexity is at least \(\Omega(\sqrt{s})\), where \(s\) is the optimal complexity of a vanilla tester. But there exist prover strategies that can beat this lower bound.

**Wasserstein Identity Testing**, by Shichuan Deng, Wenzheng Li, and Xuan Wu (arXiv). For Take 2 on distribution testing, this paper considers the problem on continuous distributions. In all results on distribution testing, the sample complexity depends on the support size. This breaks down in this setting, so this paper focuses on identity testing according to Wasserstein distance (as opposed to \(\ell_p\)-norms). A previous paper of Do Ba-Nguyen-Nguyen-Rubinfeld also considers the same problem, where the domain is assumed to be \([\Delta]^d\). In this paper, we assume that the domain \(X\) is endowed with a metric space, to allow for the definition of Wasserstein/earthmover distance between distributions. The final result is technical, depends on the size of nets in \(X\), and is shown to be optimal for testing equality with a known distribution \(p\). The paper also gives an instance optimal for (almost) all \(p\), a la Valiant-Valiant for the discrete case.

**Improved Bounds for Testing Forbidden Order Patterns**, by Omri Ben-Elizer and Clément Canonne (arXiv). Any function from \(f:D \to \mathbb{R}\), where \(D\) is a total order, can be thought to induce a permutation, based on the order of function values. Consider \(f:[n] \to \mathbb{R}\) and permutation \(\pi \in S_k\). The property of \(\pi\)-free permutations is the set of \(f\) such that no restriction of \(f\) (to a subdomain of size \(k\)) induces \(\pi\). Newman et al proved that this property can be tested non-adaptively in (ignoring \(\varepsilon\)) \(O(n^{1-1/k})\) samples. Furthermore, for non-monotone \(\pi\), there is a non-adaptive \(\Omega(\sqrt{n})\) lower bound. This paper has a number of results that shed significant light on the non-adaptive complexity. The upper bound is improved to \(O(n^{1-1/(k-1)})\), and there exist a class of permutations (for every \(k\)) for which this is the optimal complexity. Furthermore, for a random \(\pi\), the paper shows a lower bound of \(\Omega(n^{1-1/(k-3)})\). There is an intriguing conjecture on the optimal complexity for any \(k\), that has an intricate dependence on the structure of \(k\). On the adaptive side, there is an interesting hierarchy for testing \((1,3,2)\)-freeness, depending on the number of rounds of adaptivity. There is an \(O(n^{1/(r+1)})\) tester with \(r\)-rounds of adaptivity, and any such tester requires \(O(n^{1/2^{r+3}})\) queries.

**A \(o(d)\cdot \mathrm{poly}\log n\) Monotonicity Tester for Boolean Functions
over the Hypergrid \([n]^d\)**, by Hadley Black, Deeparnab Chakrabarty, and C. Seshadhri (arXiv). Monotonicity testing has seen much progress in the past few years. This paper focuses on monotonicity testing over the hypergrid, of functions \(f:[n]^d \to \{0,1\}\). For \(n=2\) (the hypercube), the result of Khot-Minzer-Safra gives a \(\widetilde{\sqrt{d}}\) time (non-adaptive) tester. Previously, the best known tester for general \(n\) took \(O(d\log d)\) queries, by Berman-Raskhodnikova-Yaroslavtsev. This paper breaks the barrier of \(d\) queries for the hypergrid, by giving a \(O(d^{5/6}\log n)\) time tester. The main technical ingredient is a new isoperimetric inequality for the directed “augmented” hypergrid, where pairs differing on one coordinate by a power of 2 are also connected. The intriguing open question that remains is the existence of testers with query complexity sublinear in \(d\)

**Provable and practical approximations for the degree distribution using sublinear graph samples**, by Talya Eden, Shweta Jain, Ali Pinar, Dana Ron, and C. Seshadhri (arXiv). The degree distribution of a simple, undirected graph \(G\) is the sequence \(\{n_d\}\), where \(n_d\) is the number of vertices of degree \(d\). The properties of the degree distribution have played a significant role in network science and the mathematical study of real-world graphs. It is often the first quantity computed in a “data analysis” of a massive graph. This paper addresses the problem of getting (bicriteria) estimates for all values of the degree distribution, in sublinear time. The main result gives sublinear time algorithms for computing degree distributions with sufficiently “fat” tails, as measured by certain fatness indices. For the important special case, when the degree distribution is a power law (\(n_d \propto 1/d^{\gamma}\)), this result yields strictly sublinear algorithms. Interestingly, the main result involves the h-index of the degree sequence, inspired by bibliographic metrics. This problem is practically important, and the paper demonstrates the quality of the sublinear algorithm in practice.

**On the Complexity of Sampling Vertices Uniformly from a Graph**, by Flavio Chierichetti and Shahrzad Haddadan (arXiv). This paper isn’t your traditional property testing paper, but is very relevant to those of us interested in graph property testing. One common query model in graph property testing is access to uniform random vertices. In practice though (think of a graph like the Facebook friendship network or the web graph), this is quite challenging. We typically have access to a few seed vertices, and we can “crawl” the graph. A natural approach is to perform a random walk (in the hope of mixing quickly) to generate random vertices. We might attempt rejection sampling or Metropolis-Hastings on top of that to get uniform random vertices. A recent result of Chierichetti et al give an algorithm for this problem using \(O(t_{mix} d_{avg})\) samples, where \(t_{mix}\) is the mixing time (of the random walk on the graph) and \(d_{avg}\) is the average degree. This paper proves that this bound is optimal, for most reasonable choices of these parameters.