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Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 424 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade
Algorithms and Complexity Results for #SAT and Bayesian Inference
 IN 44TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS
, 2004
"... Bayesian inference is an important problem with numerous applications in probabilistic reasoning. Counting satisfying assignments is a closely related problem of fundamental theoretical importance. In this paper, we show that plain old DPLL equipped with memoization (an algorithm we call #DPLLCache) ..."
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Cited by 66 (6 self)
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) can solve both of these problems with time complexity that is at least as good as stateoftheart exact algorithms, and that it can also achieve the best known timespace tradeoff. We then proceed to show that there are instances where #DPLLCache can achieve an exponential speedup over existing
Efficient Dimensionality Reduction for Canonical Correlation Analysis
"... We present a fast algorithm for approximate Canonical Correlation Analysis (CCA). Given a pair of tallandthin matrices, the proposed algorithm first employs a randomized dimensionality reduction transform to reduce the size of the input matrices, and then applies any standard CCA algorithm to the ..."
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Cited by 3 (1 self)
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to the new pair of matrices. The algorithm computes an approximate CCA to the original pair of matrices with provable guarantees, while requiring asymptotically less operations than the stateoftheart exact algorithms. 1.
Distributing an exact algorithm for maximum clique: maximising the costup, CoRR abs/1209.4560
, 2012
"... Abstract. We take an existing implementation of an algorithm for the maximum clique problem and modify it so that we can distribute it over an adhoc cluster of machines. Our goal was to achieve a significant speedup in performance with minimal development effort, i.e. a maximum costup. We present a ..."
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Cited by 2 (2 self)
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a simple modification to a stateoftheart exact algorithm for maximum clique that allows us to distribute it across many machines. An empirical study over large hard benchmarks shows that speedups of an order of magnitude are routine for 25 or more machines. 1
Toward the next generation of recommender systems: A survey of the stateoftheart and possible extensions
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1420 (21 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Scaling Up MAP Search in Bayesian Networks Using External Memory
"... Stateoftheart exact algorithms for solving the MAP problem in Bayesian networks use depthfirst branchandbound search with bounds computed by evaluating a join tree. Although this approach is effective, it can fail if the join tree is too large to fit in RAM. We describe an externalmemory MAP se ..."
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Stateoftheart exact algorithms for solving the MAP problem in Bayesian networks use depthfirst branchandbound search with bounds computed by evaluating a join tree. Although this approach is effective, it can fail if the join tree is too large to fit in RAM. We describe an externalmemory MAP
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
The Viterbi algorithm
 Proceedings of the IEEE
, 1973
"... vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A ..."
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Cited by 985 (3 self)
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vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A799A804, 1964. [9] T. C. Mo, “Theory of electrodynamics in media in noninertial frames and applications, ” J. Math. Phys., vol. 11, pp. 25892610, 1970.
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 548 (13 self)
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has run for M steps, with M sufficiently large, the distribution governing the state of the chain approximates the desired distribution. Unfortunately it can be difficult to determine how large M needs to be. We describe a simple variant of this method that determines on its own when to stop
An Exact Algorithm for Coalition Structure Generation and Complete Set Partitioning
, 2013
"... Solving the Coalition Structure Generation problem is a major challenge in cooperative game theory. It involves partitioning the set of agents into subsets (or coalitions) such that the total reward is maximized. We study this problem in Characteristic Function Games, i.e., scenarios where every pos ..."
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Cited by 2 (1 self)
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that maximizes the profit of the auctioneer. To date, there are two stateoftheart, exact algorithms for solving this problem: (1) a dynamicprogramming algorithm called DP (Yeh, 1986; Rothkopf et al., 1995) and (2) a treesearch algorithm called IP (Rahwan et al., 2009). Each of these two algorithms has
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