. Here we provide conditions under which cost-penalized mu-tual information may achieve similar guarantees. Specifically, if the cost of a measurement is proportional to the information it conveys, the bounds proven in [4] and [10] still apply. Index Terms — information measures, sensor selection 1.

This material is based upon work supported under NSF grants CCR-9732705, CCR-0085982, CAREER Award IRI-9703122,

We consider the budget allocation problem over bipartite influence model proposed by Alon et al. (Alon et al., 2012). This problem can be viewed as the well-known influence maximization problem with budget constraints. We first show that this problem and its much more general form fall into a general setting; namely the monotone submodular function maximization over integer lattice subject to a knapsack constraint. Our framework includes Alon et al.’s model, even with a competitor and with cost. We then give a (1 − 1/e)-approximation algorithm for this more general problem. Furthermore, when influence probabili-

This document is the supplementary materials for the CVPR 2013 paper ”Group and Sparse Regularized Sparse Modeling for Dictionary Learning”. The first section con-tains derivations of the theoretical proof and of the two op-timization algorithms in the Sec. 2 of the main text. The second section

the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels

for transmission and reception, it is shown that the coherence of the data matrix is both asymptotically and approximately optimal with respect to the number of antennas, and further, the data matrix is recoverable using a subset of its entries with minimal cardinality. Sufficient conditions guaranteeing low

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For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy

We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum

. Multiresolution analysis offers a simple, unified, and theoretically sound approach to dealing with these problems. Lounsbery et al. have recently developed a technique for creating multiresolution representations for a restricted class of meshes with subdivision connectivity. Unfortunately, meshes encountered