After a quite slow month of March, things sped up quite significantly in April: six different papers, ranging from graph testing to function testing to quantum distribution testing!

**Update (05/04): **We missed one. Seven!

**Junta correlation is testable**, by Anindya De, Elchanan Mossel, and Joe Neeman (arXiv). Junta testing really has seen a lot of attention and progress over the pass couple years! In this new work, the focus is on the *tolerant* version of (Boolean) junta testing, where the goal is to decide whether a given function \(f\colon \{-1,1\}^n\to \{-1,1\}\) is close to some \(k\)-junta, or far from any such simple function. The current paper improves on previous work of Blais, Canonne, Eden, Levi, and Ron, which showed how to distinguish between \(\varepsilon\)-close to \(k\)-junta and \(16\varepsilon\)-far from \(k\)-junta in \(O_{k,\varepsilon}(1)\) queries, in two ways: (i) the gap-factor of 16 can now be made arbitrarily small, yielding the first *fully* tolerant non-trivial junta tester; and (ii) the new algorithm also identifies the underlying “core junta” (up to permutation of the coordinates). Besides the other results contained in the paper (such as results for a relaxation of the tolerant question akin to that of Blais et al.), a key aspect of this paper is to go beyond the use of (set) influence as a proxy for distance to junta-ness which underlied all previous work, thus potentially opening a new avenue towards get fully tolerant, \(\mathrm{poly}(k,1/\varepsilon)\)-query junta testing algorithms.

**Testing Tensor Products**, by Irit Dinur and Konstantin Golubev (arXiv). In this paper, the authors consider the following testing question: given query access to a Boolean function \(f\colon [n]^d \to \mathbb{F}_2\), test whether \(f\) is of the form \(f(x) = f_1(x_1)+\dots f_d(x_d)\) (equivalently, this is the task of testing whether a \(d\)-dimensional tensor with \(\pm 1\) is of rank \(1\)). They provide two different proximity-oblivious testers (POT) for this task: a \(4\)-query one, reminiscent and building upon the BLR linearity test; as well as a \((d+1)\)-query POT *(which they dub “Shapka Test,” one can assume for their love of warm and comfortable hats)*, whose analysis is significantly simpler.

Changing direction, the next paper we saw is concerned with quantum testing of (regular, good ol’ classical) probability distributions:

**Quantum Algorithms for Classical Probability Distributions**, by Aleksandrs Belovs (arXiv). This work on quantum distribution testing considers 4 of the existing models of access to samples from an unknown probability distribution, analyzes their relationship, and argues their merits. Further, the autho establishes that for the simple hypothesis testing question of distinguishing between two (known, fixed) probability distributions \(p,q\), in all four models the optimal sample complexity is proportional to the inverse of the Hellinger distance between \(p\) and \(q\)—in contrast to the classical setting, where it is known to be proportional to the *squared* inverse of this distance.

Turning to graphs, the next work considers clustering of bounded-degree graphs, a topic we recently discussed as well:

**Robust Clustering Oracle and Local Reconstructor of Cluster Structure of Graphs**, by Pan Peng (arXiv). Consider the following noisy model: an unknown bounded-degree graph (of maximum degree \(d\)) \(G\) which is \((k, \phi_{\rm in},\phi_{\rm out})\)-clusterable (i.e., clusterable in at most \(k\) clusters of inner and outer conductances bounded by \(\phi_{\rm in},\phi_{\rm out}\)) is adversarially modified in at most \($\varepsilon d n\) edges between clusters. This noise model, introduced in this work, leads to the natural questions of “recovering the underlying graph \(G\)”: this is what the author tackles, by designing sublinear-time *local clustering oracles* and *local reconstruction algorithms* (local filters) in this setting. Further, in view of the noise model reminiscent of property testing in the bounded-degree graph setting, connections to testing clusterability are discussed; the implications of the results for testing clusterability are discussed in Section 1.4.

**A Faster Local Algorithm for Detecting Bounded-Size Cuts with Applications to Higher-Connectivity Problems**, by Sebastian Forster and Liu Yang (arXiv). The authors focus on the problem of finding an edge (or vertex) cut in a given graph from a *local* viewpoint, i.e., in the setting of local computation algorithms, and provide a slew of results in detecting bounded-size cuts. This in turn has direct implications for testing \(k\)-edge and \(k\)-vertex connectivity, directed and undirected, in both the bounded-degree and general (unbounded degree) models, improving on or subsuming the current state-of-the-art across the board *(see Section 1.2.4 of the paper, and the very helpful Tables 3 and 4, for a summary of the improvements)*.

**Random walks and forbidden minors II: A \(\mathrm{poly}(d\varepsilon^−1)\)-query tester for minor-closed properties of bounded degree graphs**, by Akash Kumar, C. Seshadhri, and Andrew Stolman (ECCC). Following their breakthrough from last May, the authors are at it again! Leveraging a random-walk approach, they resolve the following open question in testing bounded-degree graph: *is there a \(\mathrm{poly}(1/\varepsilon)\)-query tester for planarity (and, more generality, for minor-closed graph properties)? *The previous state-of-the-art, due to Levi and Ron, had a quasipolynomial dependence on \(1/\varepsilon\).

Without spoiling too much: the answer to the open question is *yes—* and further the authors settle it by showing an even more general result on testing \(H\)-freeness (for any constant-size graph \(H\)).

**Update (05/04): **

**Testing Unateness Nearly Optimally**, by Xi Chen and Erik Waingarten (arXiv). Recall that a function \(f\colon \{0,1\}^n\to \{0,1\}\) is said to be unate if there exists some \(s\in\{0,1\}^n\) such that \(f(\cdot \oplus s)\) is monotone; i.e., if \(f\) is either non-increasing or non-decreasing in each coordinate. Testing unateness has seen a surge of interest over the past year or so; this work essentially settles the question, up to polylogarithmic factors in \(n\) (and the exact dependence on \(\varepsilon\)). Namely, the authors present and analyze an \(\tilde{O}(n^{2/3}/\varepsilon^2)\)-query adaptive tester for unateness, which nearly matches the \(\tilde{\Omega}(n^{2/3})\)-query lower bound for adaptive testers previously established by Chen, Waingarten, and Xie.