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1,688
Polynomial averages converge to the product of the integrals
 Isr. J. Math
"... Abstract. We answer a question posed by Vitaly Bergelson, showing that in a totally ergodic system, the average of a product of functions evaluated along polynomial times, with polynomials of pairwise differing degrees, converges in L 2 to the product of the integrals. Such averages are characterize ..."
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Cited by 20 (11 self)
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Abstract. We answer a question posed by Vitaly Bergelson, showing that in a totally ergodic system, the average of a product of functions evaluated along polynomial times, with polynomials of pairwise differing degrees, converges in L 2 to the product of the integrals. Such averages
SPhT 92/073 Polynomial Averages in the Kontsevich Model
, 1992
"... We obtain in closed form averages of polynomials, taken over hermitian matrices with the Gaussian measure involved in the Kontsevich integral, and prove a conjecture of Witten enabling one to express analogous averages with the full (cubic potential) measure, as derivatives of the partition function ..."
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We obtain in closed form averages of polynomials, taken over hermitian matrices with the Gaussian measure involved in the Kontsevich integral, and prove a conjecture of Witten enabling one to express analogous averages with the full (cubic potential) measure, as derivatives of the partition
Coverage Problems in Wireless Adhoc Sensor Networks
 in IEEE INFOCOM
, 2001
"... Wireless adhoc sensor networks have recently emerged as a premier research topic. They have great longterm economic potential, ability to transform our lives, and pose many new systembuilding challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as ..."
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Cited by 441 (9 self)
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search algorithms, we establish the main highlight of the paper  optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks. I.
RMAX  A General Polynomial Time Algorithm for NearOptimal Reinforcement Learning
, 2001
"... Rmax is a very simple modelbased reinforcement learning algorithm which can attain nearoptimal average reward in polynomial time. In Rmax, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model. The mod ..."
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Cited by 297 (10 self)
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Rmax is a very simple modelbased reinforcement learning algorithm which can attain nearoptimal average reward in polynomial time. In Rmax, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model
Weighted Essentially NonOscillatory Schemes
, 1994
"... In this paper we introduce a new version of ENO (Essentially NonOscillatory) shockcapturing schemes which we call Weighted ENO. The main new idea is that, instead of choosing the "smoothest" stencil to pick one interpolating polynomial for the ENO reconstruction, we use a convex combinati ..."
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Cited by 326 (8 self)
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In this paper we introduce a new version of ENO (Essentially NonOscillatory) shockcapturing schemes which we call Weighted ENO. The main new idea is that, instead of choosing the "smoothest" stencil to pick one interpolating polynomial for the ENO reconstruction, we use a convex
A publickey cryptosystem with worstcase/averagecase equivalence
, 1997
"... Abstract We present a probabilistic public key cryptosystem which is secure unless the worst case of the following lattice problem can be solved in polynomial time: "Find the shortest nonzero vector in an n dimensional lattice L where the shortest vector v is unique in the sense that any ot ..."
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Cited by 246 (5 self)
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Abstract We present a probabilistic public key cryptosystem which is secure unless the worst case of the following lattice problem can be solved in polynomial time: "Find the shortest nonzero vector in an n dimensional lattice L where the shortest vector v is unique in the sense that any
Convergence of polynomial ergodic averages
 Isr. J. Math
"... Abstract. We prove the L2 convergence for an ergodic average of a product of functions evaluated along polynomial times in a totally ergodic system. For each set of polynomials, we show that there is a particular factor, which is an inverse limit of nilsystems, that controls the limit behavior of th ..."
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Cited by 44 (11 self)
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Abstract. We prove the L2 convergence for an ergodic average of a product of functions evaluated along polynomial times in a totally ergodic system. For each set of polynomials, we show that there is a particular factor, which is an inverse limit of nilsystems, that controls the limit behavior
Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time
, 2003
"... We introduce the smoothed analysis of algorithms, which continuously interpolates between the worstcase and averagecase analyses of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We me ..."
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Cited by 202 (12 self)
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We introduce the smoothed analysis of algorithms, which continuously interpolates between the worstcase and averagecase analyses of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We
Averaging
, 2003
"... A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the model weights are approximated by the BIC criteri ..."
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A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the model weights are approximated by the BIC
Polynomial Time Approximation Schemes for Dense Instances of NPHard Problems
, 1995
"... We present a unified framework for designing polynomial time approximation schemes (PTASs) for "dense" instances of many NPhard optimization problems, including maximum cut, graph bisection, graph separation, minimum kway cut with and without specified terminals, and maximum 3satisfiabi ..."
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Cited by 189 (35 self)
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We present a unified framework for designing polynomial time approximation schemes (PTASs) for "dense" instances of many NPhard optimization problems, including maximum cut, graph bisection, graph separation, minimum kway cut with and without specified terminals, and maximum 3
Results 1  10
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1,688