no code implementations • 18 May 2018 • Cheng Ju, James Li, Bram Wasti, Shengbo Guo
We show that the HELP algorithm improves the predictive performance across multiple tasks, together with semantically meaningful embedding that are discriminative for downstream classification or regression tasks.
no code implementations • NeurIPS 2011 • Shengbo Guo, Onno Zoeter, Cédric Archambeau
We propose a new sparse Bayesian model for multi-task regression and classification.
no code implementations • NeurIPS 2010 • Shengbo Guo, Scott Sanner, Edwin V. Bonilla
Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users' latent utility functions.