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January 14, 2011
Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate ...
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January 14, 2011
Problems involving maximization of submodular functions arise in many applications, such as combinatorial auctions and coverage optimization in wireless networks. Submodular maximization can be ...
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January 14, 2011
In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of sourcetarget vertex pairs. The goal is to orient the undirected edges so ...
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January 14, 2011
Most existing MKL approaches employ the large margin principle to learning kernels. However, we point out that the margin itself can not well describe the goodness of a kernel due to the ...
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January 14, 2011
Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection ...
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January 14, 2011
Most organisms facing a choice between multiple stimuli will look repeatedly at them, presumably implementing a comparison process between the items’ values. Little is known about the exact ...
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January 14, 2011
Ordinary and partial differential equations are extensively used in different branches of science and engineering to model wide variety of phenomena, such as diffusion, stability, wave ...
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January 14, 2011
We develop a theory of online learning by defining several complexity measures. Among them are analogues of Rademacher complexity, covering numbers and fat-shattering dimension from statistical ...
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January 13, 2011
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January 13, 2011
Structured prediction (SP) problems are characterized by strong interdependence among the output variables, usually with sequential, graphical, or combinatorial structure [17, 7]. Obtaining good ...
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January 13, 2011
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using ...
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January 13, 2011
This tutorial surveys methodology and theory for high-dimensional statistical inference when the number of variables or features greatly exceeds sample size. Particular emphasis will be placed on ...
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January 13, 2011
This tutorial surveys methodology and theory for high-dimensional statistical inference when the number of variables or features greatly exceeds sample size. Particular emphasis will be placed on ...
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January 13, 2011
The purpose of this tutorial is to explore the interplay between sensing and control, to highlight the "information knot" that ties them, and to design inference and learning algorithms to ...
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January 13, 2011
The human visual system, and almost every digital camera, achieves trichromacy by using sensors of different spectral sensitivity arranged in some spatial pattern. What pattern of spatial ...
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January 13, 2011
It was realized soon after the introduction of Multiple Kernel Learning that l1 - regularization and its groupwise extension are related to MKL by dualization. MKL can therefore be viewed as ...
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January 13, 2011
The study of the structure and function of proteins serves up many problems that offer challenges and opportunities for computational and statistical research. I will overview my experiences in ...
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January 12, 2011
The world’s market on piezoceramics is about 10 billion dollar per year. Main applications are actuators, e.g. for fuel injection and for piezomotors, sensors e.g. for parking pilot and airbag, ...
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January 07, 2011
Professor Saltzman continues his presentation on DNA technology by discussing control of gene expression using two methods of RNA silencing: anti-sense therapy and RNA interference. Molecular ...
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January 07, 2011
In this final lecture, Professor Saltzman talks about artificial organs, with a stress on synthetic biomaterials. First, the body’s responses (immunological and scar healing responses) to foreign ...
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