A Probabilistic Model for Dirty Multi-task Feature Selection
Authors: Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Several experiments show that a model based on the new robust prior provides better predictive performance than other benchmark methods. |
| Researcher Affiliation | Academia | Daniel Hern andez-Lobato DANIEL.HERNANDEZ@UAM.ES Universidad Aut onoma de Madrid, Computer Science Department, Madrid, 28049, SpainJos e Miguel Hern andez-Lobato JMH@SEAS.HARVARD.EDU Harvard University, School of Engineering and Applied Sciences, Cambridge, MA 02138, USAZoubin Ghahramani ZOUBIN@ENG.CAM.AC.UK University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK |
| Pseudocode | No | No pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | Yes | The complete details about EP are found in the supplementary material, alongside with an R implementation of the proposed method. |
| Open Datasets | Yes | The experimental protocol follows the DREAM 4 in silico challenge 2009. We consider the dataset described in (Barretina et al., 2012). We consider the problem of denoising the 256 256 house image used in (Titsias & L azaro-Gredilla, 2011) |
| Dataset Splits | Yes | We use 90% of the instances for training and 10% for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | All methods described are implemented in the R language. |
| Experiment Setup | Yes | DMFS, STL and MFS need not fix any hyper-parameters since they infer them from the data using hyper-priors. Unless stated differently, in all probabilistic models we assume different levels of noise for each task when training. In DM and RMFL we choose hyper-parameters using a grid search guided by an inner cross-validation method. |