Timezone: »

 
Poster
Nonparametric Greedy Algorithms for the Sparse Learning Problem
Han Liu · Xi Chen

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None

This paper studies the forward greedy strategy in sparse nonparametric regression. For additive models, we propose an algorithm called additive forward regression; for general multivariate regression, we propose an algorithm called generalized forward regression. Both of them simultaneously conduct estimation and variable selection in nonparametric settings for the high dimensional sparse learning problem. Our main emphasis is empirical: on both simulated and real data, these two simple greedy methods can clearly outperform several state-of-the-art competitors, including the LASSO, a nonparametric version of the LASSO called the sparse additive model (SpAM) and a recently proposed adaptive parametric forward-backward algorithm called the Foba. Some theoretical justifications are also provided.

Author Information

Han Liu (Carnegie Mellon University)
Xi Chen (NYU)

Xi Chen is an associate professor with tenure at Stern School of Business at New York University, who is also an affiliated professor to Computer Science and Center for Data Science. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University (CMU). He studies high-dimensional statistical learning, online learning, large-scale stochastic optimization, and applications to operations. He has published more than 20 journal articles in statistics, machine learning, and operations, and 30 top machine learning peer-reviewed conference proceedings. He received NSF Career Award, ICSA Outstanding Young Researcher Award, Faculty Research Awards from Google, Adobe, Alibaba, and Bloomberg, and was featured in Forbes list of “30 Under30 in Science”.

More from the Same Authors