Exponential Family Matrix Completion under Structural Constraints
Authors: Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we corroborate our theoretical findings via simulated experiments. |
| Researcher Affiliation | Academia | Suriya Gunasekar SURIYA@UTEXAS.EDU Pradeep Ravikumar PRADEEPR@CS.UTEXAS.EDU Joydeep Ghosh GHOSH@ECE.UTEXAS.EDU The University of Texas at Austin, Texas, USA |
| Pseudocode | No | The paper describes the proposed method mathematically and textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes using 'simulated datasets' where 'observation matrices, X, are then sampled from the different members of exponential family distributions parameterized by Θ', but it does not refer to or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating simulated data and uniformly sampling observations but does not specify explicit train, validation, or test dataset splits or their sizes. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies, such as libraries or solvers with their version numbers, used for the experiments. |
| Experiment Setup | Yes | We create low rank ground truth parameter matrices, Θ Rm n of sizes n {50, 100, 150, 200} (for simplicity we considered square matrices, m = n); we set the rank to r = 2 log n. The observation matrices, X, are then sampled from the different members of exponential family distributions parameterized by Θ . For each n, we uniformly sample a subset Ωentries of the observation matrix X, and estimate bΘ from (6). |