We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize the sample complexity of the protocol subject to a memory constraint, and (2) A {\em distributed model} where the data samples reside at multiple machines and the goal is to minimize the communication cost of the protocol. In both these models, we provide efficient algorithms for uniformity/identity testing (goodness of fit) and closeness testing (two sample testing). Moreover, we show nearly-tight lower bounds on (1) the sample complexity of any one-pass streaming tester for uniformity, subject to the memory constraint, and (2) the communication cost of any uniformity testing protocol, in a restricted `one-pass' model of communication.
Optimal Testing of Discrete Distributions with High Probability
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；MPI（马克斯·普朗克研究所）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Yale University（耶鲁大学）；UT Austin（德克萨斯大学奥斯汀分校）
How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem
Institute of Physics（物理研究所）；Institut de Recherche sur les Phenomènes Hors Équilibre（非平衡现象研究所）；Centrale Marseille（马塞尔中央理工学院）；CNRS（法国国家科学研究中心）；Aix-Marseille Université（艾克斯-马赛大学）
Interactive proofs for verifying (quantum) learning and testing
Department of Computer Science, University of Warwick, UK（沃里克大学计算机科学系）；Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Berlin, Germany（柏林自由大学复杂量子系统中心）；Helmholtz-Zentrum Berlin für Materialien und Energie, Berlin, Germany（柏林材料与能源研究中心）；IBM Quantum, Almaden Research Center, San Jose, CA, USA（IBM量子，阿姆登研究中心）
MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation
London Metropolitan University（伦敦 Metropolitan 大学）；Cyber Security Research Centre（网络安全研究中心）；Cybersecurity Institute（网络安全研究所）；Department of Computer Science（计算机科学系）
