News for April 2018

Three Four papers for April: a new take on linearity testing, results on sublinear algorithms with advice, histogram testing, and distributed inference problems. (Edit: Sorry Clément, for missing your paper on distributed inference!)

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.

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