Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On Regularizing Rademacher Observation Losses
Authors: Richard Nock
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,912 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,593 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |