Many researchers have attempted to predict the Enron corporate hierarchy from the data. This work, however, has been hampered by a lack of data. We present a new, large, and freel...
We resolve the problem of small-space approximate selection in random-order streams. Specifically, we present an algorithm that reads the n elements of a set in random order and ...
Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
We study the notion of regret ratio proposed in [19] to deal with multi-criteria decision making in database systems. The regret minimization query proposed in [19] was shown to h...
Danupon Nanongkai, Ashwin Lall, Atish Das Sarma, K...
We develop a new method for proving explicit approximation lower bounds for TSP problems with bounded metrics improving on the best up to now known bounds. They almost match the b...
Abstract. In this paper we find a lower bound of the second-order nonlinearities of Boolean bent functions of the form f(x) = Trn 1 (α1xd1 + α2xd2 ), where d1 and d2 are Niho ex...
Abstract. Previous works [11, 6] introduced a model of semantic communication between a “user” and a “server,” in which the user attempts to achieve a given goal for commun...
We study the problem of navigating through a database of similar objects using comparisons. This problem is known to be strongly related to the small-world network design problem....
— We study the two-user one-eavesdropper discrete memoryless compound wiretap channel, where the transmitter sends a common confidential message to both users, which needs to be...
In this paper, we provide a semi-analytical method for predicting the upper and lower bound of the end-to-end mutual information for a relay network with practical codes and diffe...