We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
We propose an information–theoretic approach to the watermark embedding and detection under limited detector resources. First, we present asymptotically optimal decision regions...
We study the impact of collusion –nepotistic linking– in a Web graph in terms of Pagerank. We prove a bound on the Pagerank increase that depends both on the reset probability...
Ricardo A. Baeza-Yates, Carlos Castillo, Vicente L...
We prove that for any positive integer k, there is a constant ck such that a randomly selected set of cknk log n Boolean vectors with high probability supports a balanced k-wise i...
-- We characterize the best achievable performance of lossy compression algorithms operating on arbitrary random sources, and with respect to general distortion measures. Direct an...