Approximation Algorithms for D-optimal Design

Experimental design is a classical statistics problem and its aim is to estimate an unknown m-dimensional vector from linear measurements where a Gaussian noise is introduced in each measurement. For the combinatorial experimental design problem, the goal is to pick k out of the given n experiments so as to make the most accurate estimate … Read more

Approximate Positively Correlated Distributions and Approximation Algorithms for D-optimal Design

Experimental design is a classical problem in statistics and has also found new applications in machine learning. In the experimental design problem, the aim is to estimate an unknown vector x in m-dimensions from linear measurements where a Gaussian noise is introduced in each measurement. The goal is to pick k out of the given … Read more