Session title: Supervised and unsupervised learning of complex data
Organizer: Junhui Wang (Citi U of HK)
Chair: Junhui Wang (Citi U of HK)
Time: June 4th, 11:00am-12:30pm
Location: VEC 405
Speech 1: Systems of partially linear models with gradient boosting
Speaker: Yongzhao Shao (NYU)
Abstract: We develop systems partially linear models with gradient boosting for prediction in multicenter studies or regression-based clustering in large scale data. Simultaneous variable selection and effect estimation are achieved using LASSO type penalty functions and ADMM. Simulation studies and real data examples are used to illustrate effectiveness of the proposed methods.
Speech 2: Supervised Dimensionality Reduction for Exponential Family Data
Speaker: Yoonkyung Lee (OSU)
Abstract: Supervised dimensionality reduction techniques, such as partial least squares and supervised principal components, are powerful tools for making predictions with a large number of variables. The implicit squared error terms in the objectives, however, make it less attractive to non-Gaussian data, either in the covariates or the responses. Drawing on a connection between partial least squares and the Gaussian distribution, we show how partial least squares can be extended to other members of the exponential family – similar to the generalized linear model – for both the covariates and the responses. Unlike previous attempts, our extension gives latent variables which are easily interpretable as linear functions of the data and is computationally efficient. In particular, it does not require additional optimization for the scores of new observations and therefore predictions can be made in real time. This is joint work with Andrew Landgraf at Battelle Memorial Institute.
Speaker: Yuan Zhang (OSU)
Abstract: