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**Published in:** LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

The online manipulation-resilient testing model, proposed by Kalemaj, Raskhodnikova and Varma (ITCS 2022 and Theory of Computing 2023), studies property testing in situations where access to the input degrades continuously and adversarially. Specifically, after each query made by the tester is answered, the adversary can intervene and either erase or corrupt t data points. In this work, we investigate a more nuanced version of the online model in order to overcome old and new impossibility results for the original model. We start by presenting an optimal tester for linearity and a lower bound for low-degree testing of Boolean functions in the original model. We overcome the lower bound by allowing batch queries, where the tester gets a group of queries answered between manipulations of the data. Our batch size is small enough so that function values for a single batch on their own give no information about whether the function is of low degree. Finally, to overcome the impossibility results of Kalemaj et al. for sortedness and the Lipschitz property of sequences, we extend the model to include t < 1, i.e., adversaries that make less than one erasure per query. For sortedness, we characterize the rate of erasures for which online testing can be performed, exhibiting a sharp transition from optimal query complexity to impossibility of testability (with any number of queries). Our online tester works for a general class of local properties of sequences. One feature of our results is that we get new (and in some cases, simpler) optimal algorithms for several properties in the standard property testing model.

Omri Ben-Eliezer, Esty Kelman, Uri Meir, and Sofya Raskhodnikova. Property Testing with Online Adversaries. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 11:1-11:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{beneliezer_et_al:LIPIcs.ITCS.2024.11, author = {Ben-Eliezer, Omri and Kelman, Esty and Meir, Uri and Raskhodnikova, Sofya}, title = {{Property Testing with Online Adversaries}}, booktitle = {15th Innovations in Theoretical Computer Science Conference (ITCS 2024)}, pages = {11:1--11:25}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-309-6}, ISSN = {1868-8969}, year = {2024}, volume = {287}, editor = {Guruswami, Venkatesan}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.11}, URN = {urn:nbn:de:0030-drops-195395}, doi = {10.4230/LIPIcs.ITCS.2024.11}, annote = {Keywords: Linearity testing, low-degree testing, Reed-Muller codes, testing properties of sequences, erasure-resilience, corruption-resilience} }

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**Published in:** LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)

In recent years there has been great interest in networks of passive, computationally-weak nodes, whose interactions are controlled by the outside environment; examples include population protocols, chemical reactions networks (CRNs), DNA computing, and more. Such networks are usually studied under one of two extreme regimes: the schedule of interactions is either assumed to be adversarial, or it is assumed to be chosen uniformly at random. In this paper we study an intermediate regime, where the interaction at each step is chosen from some not-necessarily-uniform distribution: we introduce the definition of a (p,ε)-scheduler, where the distribution that the scheduler chooses at every round can be arbitrary, but it must have 𝓁_p-distance at most ε from the uniform distribution. We ask how far from uniform we can get before the dynamics of the model break down.
For simplicity, we focus on the 3-majority dynamics, a type of chemical reaction network where the nodes of the network interact in triplets. Each node initially has an opinion of either 𝖷 or 𝖸, and when a triplet of nodes interact, all three nodes change their opinion to the majority of their three opinions. It is known that under a uniformly random scheduler, if we have an initial gap of Ω(√{n log n}) in favor of one value, then w.h.p. all nodes converge to the majority value within O(n log n) steps.
For the 3-majority dynamics, we prove that among all non-uniform schedulers with a given 𝓁_1- or 𝓁_∞-distance to the uniform scheduler, the worst case is a scheduler that creates a partition in the network, disconnecting some nodes from the rest: under any (p,ε)-close scheduler, if the scheduler’s distance from uniform only suffices to disconnect a set of size at most S nodes and we start from a configuration with a gap of Ω(S+√{n log n}) in favor of one value, then we are guaranteed that all but O(S) nodes will convert to the majority value. We also show that creating a partition is not necessary to cause the system to converge to the wrong value, or to fail to converge at all. We believe that our work can serve as a first step towards understanding the resilience of chemical reaction networks and population protocols under non-uniform schedulers.

Uri Meir, Rotem Oshman, Ofer Shayevitz, and Yuval Volkov. Resilience of 3-Majority Dynamics to Non-Uniform Schedulers. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 86:1-86:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{meir_et_al:LIPIcs.ITCS.2023.86, author = {Meir, Uri and Oshman, Rotem and Shayevitz, Ofer and Volkov, Yuval}, title = {{Resilience of 3-Majority Dynamics to Non-Uniform Schedulers}}, booktitle = {14th Innovations in Theoretical Computer Science Conference (ITCS 2023)}, pages = {86:1--86:19}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-263-1}, ISSN = {1868-8969}, year = {2023}, volume = {251}, editor = {Tauman Kalai, Yael}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.86}, URN = {urn:nbn:de:0030-drops-175895}, doi = {10.4230/LIPIcs.ITCS.2023.86}, annote = {Keywords: chemical reaction networks, population protocols, randomized scheduler} }

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**Published in:** LIPIcs, Volume 185, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)

Distribution testing can be described as follows: q samples are being drawn from some unknown distribution P over a known domain [n]. After the sampling process, a decision must be made about whether P holds some property, or is far from it. The most studied problem in the field is arguably uniformity testing, where one needs to distinguish the case that P is uniform over [n] from the case that P is ε-far from being uniform (in 𝓁₁). It is known that for this task Θ(√n/ε²) samples are necessary and sufficient. This problem was recently considered in various restricted models that pose, for example, communication or memory constraints. In more than one occasion, the known optimal solution boils down to counting collisions among the drawn samples (each two samples that have the same value add one to the count). This idea dates back to the first uniformity tester, and was coined the name "collision-based tester".
In this paper, we introduce the notion of comparison graphs and use it to formally define a generalized collision-based tester. Roughly speaking, the edges of the graph indicate the tester which pairs of samples should be compared (that is, the original tester is induced by a clique, where all pairs are being compared). We prove a structural theorem that gives a sufficient condition for a comparison graph to induce a good uniformity tester. As an application, we develop a generic method to test uniformity, and devise nearly-optimal uniformity testers under various computational constraints. We improve and simplify a few known results, and introduce a new constrained model in which the method also produces an efficient tester.
The idea behind our method is to translate computational constraints of a certain model to ones on the comparison graph, which paves the way to finding a good graph: a set of comparisons allowed by the model that suffice to test for uniformity. We believe that in future consideration of uniformity testing in new models, our method can be used to obtain efficient testers with minimal effort.

Uri Meir. Comparison Graphs: A Unified Method for Uniformity Testing. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 17:1-17:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{meir:LIPIcs.ITCS.2021.17, author = {Meir, Uri}, title = {{Comparison Graphs: A Unified Method for Uniformity Testing}}, booktitle = {12th Innovations in Theoretical Computer Science Conference (ITCS 2021)}, pages = {17:1--17:20}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-177-1}, ISSN = {1868-8969}, year = {2021}, volume = {185}, editor = {Lee, James R.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.17}, URN = {urn:nbn:de:0030-drops-135560}, doi = {10.4230/LIPIcs.ITCS.2021.17}, annote = {Keywords: Distribution Testing, Uniformity Testing, Distributed Algorithms, Streaming Algorithms, Comparison Graphs} }

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**Published in:** LIPIcs, Volume 179, 34th International Symposium on Distributed Computing (DISC 2020)

Smoothed analysis is a framework suggested for mediating gaps between worst-case and average-case complexities. In a recent work, Dinitz et al. [Distributed Computing, 2018] suggested to use smoothed analysis in order to study dynamic networks. Their aim was to explain the gaps between real-world networks that function well despite being dynamic, and the strong theoretical lower bounds for arbitrary networks. To this end, they introduced a basic model of smoothing in dynamic networks, where an adversary picks a sequence of graphs, representing the topology of the network over time, and then each of these graphs is slightly perturbed in a random manner.
The model suggested above is based on a per-round noise, and our aim in this work is to extend it to models of noise more suited for multiple rounds. This is motivated by long-lived networks, where the amount and location of noise may vary over time. To this end, we present several different models of noise. First, we extend the previous model to cases where the amount of noise is very small. Then, we move to more refined models, where the amount of noise can change between different rounds, e.g., as a function of the number of changes the network undergoes. We also study a model where the noise is not arbitrarily spread among the network, but focuses in each round in the areas where changes have occurred. Finally, we study the power of an adaptive adversary, who can choose its actions in accordance with the changes that have occurred so far. We use the flooding problem as a running case-study, presenting very different behaviors under the different models of noise, and analyze the flooding time in different models.

Uri Meir, Ami Paz, and Gregory Schwartzman. Models of Smoothing in Dynamic Networks. In 34th International Symposium on Distributed Computing (DISC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 179, pp. 36:1-36:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{meir_et_al:LIPIcs.DISC.2020.36, author = {Meir, Uri and Paz, Ami and Schwartzman, Gregory}, title = {{Models of Smoothing in Dynamic Networks}}, booktitle = {34th International Symposium on Distributed Computing (DISC 2020)}, pages = {36:1--36:16}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-168-9}, ISSN = {1868-8969}, year = {2020}, volume = {179}, editor = {Attiya, Hagit}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2020.36}, URN = {urn:nbn:de:0030-drops-131145}, doi = {10.4230/LIPIcs.DISC.2020.36}, annote = {Keywords: Distributed dynamic graph algorithms, Smoothed analysis, Flooding} }