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Foundations of Computer Science Seminar

MondayMar 26, 201814:30
Foundations of Computer Science SeminarRoom 155
Speaker:Gal Shahaf Title:Oblivious routing via random walksAbstract:opens in new windowin html    pdfopens in new window

We present novel oblivious routing algorithms for the splittable and the unsplittable multicommodity flow settings. Our algorithms for both models improve upon the state-of-the-art, in terms of running time and performance with respect to graphs that exhibit good expansion guarantees. As an intermediate step towards the unsplittable setting, we present a novel generalization of Valiant's classical load balancing scheme for packet-switched networks to arbitrary graphs, which is of independent interest. Our approach relies on diffusing traffic throughout the network and then regathering it to its destination, via iterative applications of the random walk operator. Consequently, the performance guarantees of our algorithms are derived from the convergence of the random walk operator to the stationary distribution and are expressed in terms of the spectral gap of the graph (which dominates the mixing time).

MondayMay 07, 201814:30
Foundations of Computer Science SeminarRoom 155
Speaker:Yuval Dagan Title:Detecting Correlations with Little Memory and CommunicationAbstract:opens in new windowin html    pdfopens in new window

We study the problem of identifying correlations in multivariate data, under information constraints:
Either on the amount of memory that can be used by the algorithm, or the amount of communi- cation when the data is distributed across several machines. We prove a tight trade-off between the memory/communication complexity and the sample complexity, implying (for example) that to detect pairwise correlations with optimal sample complexity, the number of required mem-ory/communication bits is at least quadratic in the dimension. Our results substantially improve those of Shamir (2014), which studied a similar question in a much more restricted setting. To the best of our knowledge, these are the first provable sample/memory/communication trade-offs for a practical estimation problem, using standard distributions, and in the natural regime where the memory/communication budget is larger than the size of a single data point. To derive our theorems, we prove a new information-theoretic result, which may be relevant for studying other information-constrained learning problems.
Joint work with Ohad Shamir