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Optimal Unambiguous DNFs and Alon-Saks-Seymour

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We construct unambiguous DNFs having width $O(n)$ but $0$-certificate complexity $Ω(n^2)$. By utilizing the special structure of these DNFs, we prove a lifting theorem with a constant-sized gadget that lifts the DNF to a communication problem, while losslessly translating the separation in certificate complexity to a separation in communication complexity.

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arXiv Machine Learning
Jul 28

Learning Distributions from Multiple Data Providers

arXiv:2607. 24732v1 Announce Type: cross Abstract: Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples.

By Jon Kleinberg, Amin Saberi, Xizhi Tan, Grigoris Velegkas