High-throughput virtual screening by using a combination of first-principles calculations and Bayesian optimization (BO) has attracted much attention as method for efficient material exploration. The purpose the is often to search materials whose properties meet certain target criterion, while conventional BO aims find global extremum. Some recent works use converting such motivation. On other ...

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INTRODUCTION In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories are typically created in the hope that through analysis, such as data mining and decision support, they will yield new insights into the data and the real-world processes that created it. In practice, however, while the collecti...

SUMMARY In this paper we investigate the effects of different sampling operators on the performance of sparse reconstruction methods. The common paradigm in seismic data processing is to favor regular sampling. We will show, however, that regular sampling often hampers our data recovery efforts. Random sampling, on the other hand, can lead to algorithms where the reconstruction is almost perfec...

The Nyström method is an efficient technique to generate low-rank matrix approximations and is used in several large-scale learning applications. A key aspect of this method is the procedure according to which columns are sampled from the original matrix. In this work, we explore the efficacy of a variety of fixed and adaptive sampling schemes. We also propose a family of ensemble-based samplin...