Michael Mitzenmacher

TL;DR: This work characterizes the feasibility of graph watermarking in terms of keygen, marking, and identification functions defined over graph families with known distributions, and demonstrates the strength of this approach with exemplary watermarked schemes for two random graph models, the classic Erd\H{o}s-R\'{e}nyi model and a random power-law graph model, both of which are used to model real-world networks.

TL;DR: The adaptive cuckoo filter is introduced, a data structure for approximate set membership that extends cuckoos filters by reacting to false positives, removing them for future queries by presenting both a theoretical model for the false positive rate and simulations using both synthetic data sets and real packet traces.

TL;DR: The analyses demonstrate the importance of considering timeliness in determining the competitive ratio of an on-line algorithm and demonstrate that there exist algorithms with small competitive ratios even when large delays affect the timelness of information and the effect of decisions.

TL;DR: It is found that even small asymmetries across players (cheaper bids, better estimates of other players' intent, different valuations of items, committed players willing to play "chicken") can increase the auction duration significantly and thus skew the auctioneer's profif disproportionately.

TL;DR: A Bayesian view on the count-min sketch is presented, using the same data structure, but providing a posterior distribution over the frequencies that characterizes the uncertainty arising from the hash-based approximation, and it is shown that it is possible to straightforwardly compute posterior marginals of the unknown true counts.