Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On Regularizing Rademacher Observation Losses
Authors: Richard Nock
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with a readily available code display that regularization significantly improves rado-based learning and compares favourably with example-based learning. and 5 and 6 respectively present experiments, and conclude. |
| Researcher Affiliation | Collaboration | Richard Nock Data61, The Australian National University & The University of Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 Ω-R.ADABOOST and Algorithm 2 Ω-WL |
| Open Source Code | No | Footnote 4 states 'Code available at: http://users.cecs.anu.edu.au/ rnock/', which points to a personal homepage rather than a specific code repository for the methodology. |
| Open Datasets | Yes | The complete results aggregate experiments on twenty (20) domains, all but one coming from the UCI [Bache and Lichman, 2013] (plus the Kaggle competition domain Give me some credit ) |
| Dataset Splits | Yes | The experimental setup is a ten-folds stratified cross validation for all algorithms and each domain. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') used for the implementation or experiments. |
| Experiment Setup | Yes | All algorithms are run for a total of T = 1000 iterations, and at the end of the iterations, the classifier in the sequence that minimizes the empirical loss is kept. and To obtain very sparse solutions for regularized-ADABOOST, we pick its ω (β in [Xi et al., 2009]) in {10-4, 1, 104}. and The experimental setup is a ten-folds stratified cross validation for all algorithms and each domain. |