Learning and Data Selection in Big Datasets
Authors: Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden 2COMELEC Department, Telecom Paris Tech, Paris, France. |
| Pseudocode | Yes | Algorithm 1 Alternating Data Selection and Function Approximation (DF) |
| Open Source Code | No | The paper does not provide concrete access to its source code. It only references third-party tools and public datasets. |
| Open Datasets | Yes | Table 1: Databases for regression task. d is the input dimension. Database # Training samples # Test samples d Bodyfat 168 84 14 Housing 337 169 13 Space-ga 2,071 1,036 6 Year Prediction MSD 463,715 51,630 90 Power Consumption 1,556,445 518,814 9 Stat Lib Repository. [Online] http://lib.stat.cmu.edu/datasets/, Accessed: 2019-01-22. UCI Machine Learning Repository. [Online] http://mlr.cs.umass.edu/ml, Accessed: 2019-01-22. |
| Dataset Splits | Yes | Table 1: Databases for regression task. d is the input dimension. Database # Training samples # Test samples d Bodyfat 168 84 14 Housing 337 169 13 Space-ga 2,071 1,036 6 Year Prediction MSD 463,715 51,630 90 Power Consumption 1,556,445 518,814 9 |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | Grant, M. and Boyd, S. CVX: Matlab software for disciplined convex programming, version 2.1, March 2014. [Online] http://cvxr.com/cvx, Accessed: 201901-22. |
| Experiment Setup | No | The paper describes the model architecture and regularization (Tikonov regularization with parameter λ) but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |