Sparse Parameter Recovery from Aggregated Data
Authors: Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic data are provided in support of these theoretical claims. We also show that parameter estimation from aggregated data approaches the accuracy of parameter estimation obtainable from non-aggregated or individual samples, when applied to two real world healthcare applicationspredictive modeling of CMS Medicare reimbursement claims, and modeling of Texas State healthcare charges. |
| Researcher Affiliation | Academia | Avradeep Bhowmik AVRADEEP.1@UTEXAS.EDU Joydeep Ghosh GHOSH@ECE.UTEXAS.EDU The University of Texas at Austin, TX, USA Oluwasanmi Koyejo SANMI@ILLINOIS.EDU Stanford University, CA & University of Illinois at Urbana Champaign, IL, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. No specific repository link, explicit code release statement, or mention of code in supplementary materials is found. |
| Open Datasets | Yes | Our first dataset is the CMS Beneficiary Summary (DESyn PUF) dataset [DESyn PUF 2008] which is a public use dataset created by the Centers for Medicare and Medicaid Services and is often used for testing different data mining or statistical inferential methods before getting access to full Medicare data. Our second dataset is the Texas Inpatient Discharge dataset (Tx ID) from the Texas Department of State Health Services ([Tx ID 2014], see also [Park & Ghosh 2014]). |
| Dataset Splits | Yes | We use a LASSO estimator (with parameter chosen via cross-validation) on the full dataset to obtain a sparse regression parameter βfull. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. No information about the computational environment is provided. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. It mentions using a 'LASSO estimator' but no associated software or version. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. While it describes the general approach for synthetic and real data, it lacks numerical specifics for parameters (e.g., k for k-means, specific values for 'increasing number of datapoints', or LASSO regularization parameter values). |