Bilevel learning of the Group Lasso structure
Authors: Jordan Frecon, Saverio Salzo, Massimiliano Pontil
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of the proposed approach are quantitatively assessed on synthetic data in Section 4, and shown to favorably compare against standard approaches. In addition, an application to real data in the context of gene expression analysis is provided with the goal of discovering functional groups. In this section, we first devise synthetic experiments to illustrate and assess the performance of the proposed method. Then, we tackle a real-data experiment in the context of gene expression analysis. |
| Researcher Affiliation | Academia | 1 Computational Statistics and Machine Learning, Istituto Italiano di Tecnologia (Italy) 2 Department of Computer Science, University College London (UK) |
| Pseudocode | Yes | Algorithm 1 Dual forward-backward with Bregman distances: FBB-GLasso(y, X, λ, θ) |
| Open Source Code | No | A MATLAB R toolbox is available upon request to the authors. |
| Open Datasets | Yes | In this section, we lead a preliminary experiment on gene expression data collected from https://www.ensembl.org/ using Bio Mart. |
| Dataset Splits | Yes | The data set is split into training, validation and test sets of 20 genes each. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing specifications used for experiments. |
| Software Dependencies | No | The paper mentions a 'MATLAB R toolbox' but does not specify any version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | We consider the convex relaxation pointed in Remark 2.1, set (Q = 500, ϵ = 10 3, γ = 0.1, K = 2000) and denote the proposed solution as θBi GL. |