Event Date
SPEAKER: Pengkun Yang, Postdoctoral Research Associate, Princeton University
TITLE: “Algorithmic approaches to high-dimensional statistics”
ABSTRACT: Big datasets are often accompanied by high-dimensional statistical inferences arising from modern engineering problems such as natural language processing, computer vision, genetics, and neuroscience. High-dimensionality has significantly challenged traditional statistical theory in that classical methods can break down drastically due to either high computational cost or low statistical accuracy. Can one devise new strategies and develop new algorithms to improve both statistical and computational efficiencies?
In this talk, I will present a principled way of designing and analyzing algorithms using approximation-theoretic methods. To overcome the aforementioned challenges, the main ingredient is the choice of complexity parameters to balance the approximation error and the estimation error, which leads to efficient algorithms with provable optimality guarantees. More fundamentally, via duality between best approximation and moment matching, tight lower bounds can be naturally derived. I will discuss successful applications in property estimation, learning mixture models, and training overparametrized neural networks as examples to show the power of the proposed principle in solving challenging problems in a variety of engineering fields.
DATE: Friday, January 24th, 1:40pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 1:30pm MSB 1147