Partition-wise Linear Models
Authors: Hidekazu Oiwa, Ryohei Fujimaki
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our partition-wise linear models perform better than or are at least competitive with state-of-the-art region-specific or locally linear models. |
| Researcher Affiliation | Collaboration | Hidekazu Oiwa Graduate School of Information Science and Technology The University of Tokyo hidekazu.oiwa@gmail.com Ryohei Fujimaki NEC Laboratories America rfujimaki@nec-labs.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It refers to details of the algorithm in a 'full version' [14]. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It only references third-party libraries and code for comparison. |
| Open Datasets | Yes | We utilized several standard benchmark datasets from UCI datasets (skin, winequality, census income, twitter, internet ad, energy heat, energy cool, communities), libsvm datasets (a1a, breast cancer), and LIACC datasets (abalone, kinematics, puma8NH, bank8FM). |
| Dataset Splits | Yes | Hyperparameters9 were optimized through 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'libsvm package' and 'scikit-learn package' but does not specify their version numbers or other specific software dependencies with versions. |
| Experiment Setup | Yes | Hyper-parameters3 were set as λ0 = 0.01 and λP = 0.001. The algorithm was run in 1, 000 iterations. |