coincidence-based test for uniformity given very

Abstract. This paper introduces Monte Carlo *-Minimax Search (MCMS), a Monte-Carlo search algorithm for finite, turned based, stochastic, two-player, zero-sum games of perfect information. Through a combination of sparse sampling and classical pruning techniques, MCMS allows deep plans

In the article A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes, Kearns et al show that there’re now theoratical boundary’s to solve an mdp with an infinite state space in a such a way that the running time has no dependency’s on the size of the statespace

Sparsely sampled irregular arrays and random arrays have been used or proposed in several fields such as radar, sonar, ultrasound imaging, and seismics. We start with an introduction to array processing and then consider the combinatorial problem of finding the best layout of elements in sparse 1-D

particularly suitable for use with sparse sampling techniques. To demonstrate the utility of this method, we collected a high resolu-tion 4-D ds-TNT spectrum of a 23 kDa protein using randomized concentric shell sampling (RCSS), and we used FFT-CLEAN processing for further reduction of aliasing artifacts

We develop a flexible model-based procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates

We present an efficient “sparse sampling ” technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration versus exploitation tradeoff. Our approach combines sparse sampling with Bayesian exploration to achieve improved decision making

for the number of transiting planets that may be discovered in the Pan-STARRS Medium Deep and 3π surveys. Our search for transiting planets and M-dwarf eclipsing binaries in the SDSS-II supernova data is used to illustrate the problems (and successes) in using sparsely sampled surveys.

The next generation of galaxy surveys, aiming to observe millions of galaxies, are ex-pensive both in time and cost. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, it was shown that a sparse sampling strategy could be a powerful

We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is based on empirical divergence maximization, an approach akin to the well-known technique of empirical risk minimization. We