This month has been comparatively slow, with only five papers. I suppose we’re lucky that five papers is considered to be a slow month!

Local Testing and Decoding of High-Rate Error-Correcting Codes, by Swastik Kopparty and Shubhangi Saraf (ECCC). This is a survey article, covering recent results in locally testable, correctable, and decodable codes. A good place to start for any who have fallen behind on the recent literature.

Sample-Optimal Identity Testing with High Probability, by Ilias Diakonikolas, Themis Gouleakis, John Peebles, and Eric Price (arXiv). Distribution testing results are  often stated in the regime where the probability of failure is some constant, i.e. \(\delta = 1/3\). These can be boosted to arbitrarily high probability of success at a multiplicative cost of \(\log (1/\delta)\) using the “median trick” — repeat the test \(\log (1/\delta)\) times and choose the majority result. This paper shows that this method is suboptimal for identity testing with small values of \(\delta\). In particular, they give upper and lower bounds for the entire tradeoff curve between \(n, \varepsilon\), and \(\delta\).

Sample Efficient Estimation and Recovery in Sparse FFT via Isolation on Average, by Michael Kapralov (arXiv). The Sparse Fourier Transform problem has seen a wealth of study in the past few years, with various tradeoffs between sample and time complexity. This paper gives the first result achieving the optimal sample complexity of \(O(k \log n)\) while maintaining a time complexity which is sublinear in the length of the signal, \(n\). In order to obtain this, the author introduces a new technique for analyzing noisy hashing schemes.

Generalized Uniformity Testing, by Tuğkan Batu and Clément L. Canonne (arXiv). Uniformity testing is one of the most studied problems in distribution testing, and it is well known that the complexity is \(\Theta(\sqrt{n})\). This paper studies a slightly different question: given samples from a distribution \(p\), is it uniform over some (unknown) subset of its domain? The authors give upper and lower bounds for this question, showing that the complexity is \(\Theta(1/\|p\|_3)\). Interestingly, when \(p\) is uniform over a set of size \(\Omega(n)\), the complexity is \(\Theta(n^{2/3})\), which is more than the \(\Theta(\sqrt{n})\) cost of vanilla uniformity testing.

Boolean Unateness Testing with \(\tilde O(n^{3/4})\) Adaptive Queries, by Xi Chen, Erik Waingarten, and Jinyu Xie (arXiv). At this point, we have a fairly mature understanding of testing monotonicity over the Boolean hypercube: for non-adaptive algorithms, the complexity is \(\tilde \Theta(\sqrt{n})\). There exists a gap for adaptive algorithms — the best known lower bound is \(\tilde \Omega(n^{1/3})\), while the best upper bound is the same as for the non-adaptive case \(\tilde O(n^{1/2})\). However, many conjecture that adaptivity does not help for monotonicity testing. More recently, there has been study of testing unateness — a function is unate if it is monotone non-decreasing or non-increasing with respect to each coordinate. Interestingly, this work proves an adaptive upper bound of \(\tilde O(n^{2/3})\) which beats the lower bound of \(\tilde \Omega(n)\) for non-adaptive algorithms, thus showing that adaptivity does help for unateness testing.