Arkadas Ozakin

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Kernel density estimation is the most widely-used practical method for accurate nonparametric density estimation. However, long-standing worst-case theoretical results showing that its performance worsens exponentially with the dimension of the data have quashed its application to modern high-dimensional datasets for decades. In practice, it has been(More)
Acknowledgments Pursuing a Ph.D. is typically a lengthy endeavor, but in my case " lengthy " is something of an understatement. As such, coming up with a list of people I have met during my time here, and wish to acknowledge, is daunting to say the least. Here follows a no doubt incomplete attempt. Firstly, I would like to thank my acting advisor and(More)
—A critical open problem in ab initio protein folding is protein energy function design, which pertains to defining the energy of protein conformations in a way that makes folding most efficient and reliable. In this paper, we address this issue as a weight optimization problem and utilize a machine learning approach, learning-to-rank, to solve this(More)
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