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THEORETICAL GUARANTEES ON PENALIZED INFORMATION GATHERING
"... Optimal measurement selection for inference is combinatorially complex and intractable for large scale problems. Under mild technical conditions, it has been proven that greedy heuristics combined with conditional mutual information rewards achieve performance within a factor of the optimal. Here ..."
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. Here we provide conditions under which costpenalized mutual 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.
Theoretical guarantees for algorithms in multiagent settings
, 2004
"... This material is based upon work supported under NSF grants CCR9732705, CCR0085982, CAREER Award IRI9703122, ..."
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Cited by 6 (1 self)
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This material is based upon work supported under NSF grants CCR9732705, CCR0085982, CAREER Award IRI9703122,
1.1. Simple Proof of the Theoretical Guarantee
"... This document is the supplementary materials for the CVPR 2013 paper ”Group and Sparse Regularized Sparse Modeling for Dictionary Learning”. The first section contains derivations of the theoretical proof and of the two optimization algorithms in the Sec. 2 of the main text. The second section con ..."
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This document is the supplementary materials for the CVPR 2013 paper ”Group and Sparse Regularized Sparse Modeling for Dictionary Learning”. The first section contains derivations of the theoretical proof and of the two optimization algorithms in the Sec. 2 of the main text. The second section
Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm
, 2014
"... 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 wellknown influence maximization problem with budget constraints. We first show that this problem and its much more general form fall into a genera ..."
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Cited by 4 (1 self)
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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 wellknown 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
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 604 (15 self)
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the ability to use highdimensional feature spaces, and from their strong theoretical guarantees. However, many realworld tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernelbased methods ignore structure in the problem, assigning labels
Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees
, 2013
"... It was recently shown that low rank Matrix Completion (MC) theory can support the design of new sampling schemes in the context of MIMO radars, enabling significant reduction of the volume of data required for accurate target detection and estimation. Based on the data received, a matrix can be for ..."
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Cited by 9 (7 self)
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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
A greedy algorithm for aligning DNA sequences
 J. COMPUT. BIOL
, 2000
"... 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 a ..."
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Cited by 585 (16 self)
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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
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... 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 and c ..."
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Cited by 774 (20 self)
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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 of Arbitrary Meshes
, 1995
"... In computer graphics and geometric modeling, shapes are often represented by triangular meshes. With the advent of laser scanning systems, meshes of extreme complexity are rapidly becoming commonplace. Such meshes are notoriously expensive to store, transmit, render, and are awkward to edit. Multire ..."
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Cited by 600 (16 self)
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. 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
Maximizing the Spread of Influence Through a Social Network
 In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in gametheoretic settings, and the effects of ..."
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Cited by 990 (7 self)
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Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in gametheoretic settings, and the effects
Results 1  10
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