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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |