Noa Avigdor-Elgrabli

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We design and analyze an online reordering buffer management algorithm with improved <i>O</i>(log&thinsp;<i>k</i>/log&thinsp;log&thinsp;<i>k</i>) competitive ratio for nonuniform costs, where <i>k</i> is the buffer size. This improves on the best previous result (even for uniform costs) of Englert and Westermann (2005) giving <i>O</i>(log&thinsp;<i>k</i>)(More)
In the reordering buffer management problem (RBM) a sequence of n colored items enters a buffer with limited capacity k. When the buffer is full, one item is removed to the output sequence, making room for the next input item. This step is repeated until the input sequence is exhausted and the buffer is empty. The objective is to find a sequence of removals(More)
We give an O(log log k)-competitive randomized online algorithm for reordering buffer management, where k is the buffer size. Our bound matches the lower bound of Adamaszek et al. (STOC 2011). Our algorithm has two stages which are executed online in parallel. The first stage computes deterministically a feasible fractional solution to an LP relaxation for(More)
In this work we study the problem of Bipartite Correlation Clustering (BCC), a natural bipartite counterpart of the well studied Correlation Clustering (CC) problem. Given a bipartite graph, the objective of BCC is to generate a set of vertex-disjoint bi-cliques (clusters) which minimizes the symmetric difference to it. The best known approximation(More)
The item cold-start problem is of a great importance in collaborative filtering (CF) recommendation systems. It arises when new items are added to the inventory and the system cannot model them properly since it relies solely on historical users' interactions (e.g., ratings). Much work has been devoted to mitigate this problem mostly by employing hybrid(More)
Reordering buffer management (RBM) is an elegant theoretical model that captures the tradeoff between buffer size and switching costs for a variety of reordering/sequencing problems. In this problem, colored items arrive over time, and are placed in a buffer of size k. When the buffer becomes full, an item must be removed from the buffer. A penalty cost is(More)
Bipartite Correlation clustering is the problem of generating a set of disjoint bi-cliques on a set of nodes while minimizing the symmetric difference to a bipartite input graph. The number or size of the output clusters is not constrained in any way. The best known approximation algorithm for this problem gives a factor of 11. This result and all previous(More)
Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind(More)
We study a natural generalization of the correlation clustering problem to graphs in which the pairwise relations between objects are categorical instead of binary. This problem was recently introduced by Bonchi et al. under the name of chromatic correlation clustering, and is motivated by many real-world applications in data-mining and social networks,(More)