Sparse meta-Gaussian information bottleneck
Authors: Melani Rey, Volker Roth, Thomas Fuchs
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments Simulation: Comparison between different IB methods. We assess the efficiency of the different compression matrices A obtained by the above methods on a test set with 5000 observations. Each experiment is repeated to obtain the 50 curves for each method (shown in the top panel of Figure 2). 4.1. Real data |
| Researcher Affiliation | Collaboration | M elanie Rey MELANIE.REY@UNIBAS.CH University of Basel, Basel, Switzerland Thomas J. Fuchs FUCHS@CALTECH.EDU Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA Volker Roth VOLKER.ROTH@UNIBAS.CH University of Basel, Basel, Switzerland |
| Pseudocode | Yes | Algorithm 1 Optimisation of sparse MGIB |
| Open Source Code | Yes | A Matlab code for this method is available online1. Our algorithm (available in the supplementary material along with our R implementation) |
| Open Datasets | Yes | We generate training samples with n = 1000 observations (xi, yi), i = 1, . . . , n and dimensions fixed to p = 15, q = 15. Data was available in the form of immunohistochemical (IHC) expressions of 70 candidate biomarkers measured for 364 patients. A first promising approach to identify biomarkers important for survival prediction was reported in Meyer et al. (2012). |
| Dataset Splits | No | The paper mentions training and test sets but does not provide specific details on a validation split (percentages, counts, or explicit standard split references) for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab code' and 'R implementation' but does not specify version numbers for these software environments or any libraries used within them. |
| Experiment Setup | Yes | We generate training samples with n = 1000 observations (xi, yi)... and dimensions fixed to p = 15, q = 15. By varying the parameter κ between 0.1 and 80 we can represent I(Y ; T) as a function of I(X; T) and obtain the information curves. Data was available in the form of immunohistochemical (IHC) expressions of 70 candidate biomarkers measured for 364 patients. |