Systems | Information | Learning | Optimization
 

Instructor: Arindam Banerjee

Professor, Computer Science & Engineering, University of Minnesota, Twin Cities

www-users.cs.umn.edu/~baner029/

Estimation with Norm Regularization, with Applications to Climate Science

The talk will discuss recent advances in the analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, as well as application of such estimation to climate science. Analysis of estimation error for regularized problems needs to consider four aspects: the norm, the …

Learning with Dependent Data

Several important families of computational and statistical results in machine learning and randomized algorithms rely on statistical independence of data. The scope of such results include the Johnson-Lindenstrauss Lemma (JLL), the Restricted Isometry Property (RIP), regression models, and stochastic optimization. In this talk, we will discuss a new result on …