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.
Online multiclass boosting
Authors: Young Hun Jung, Jack Goetz, Ambuj Tewari
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,294 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,523 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 | Both algorithms not only possess theoretical proofs of mistake bounds, but also demonstrate superior performance over preexisting methods. ... We compare the new algorithms to existing ones for online boosting on several UCI data sets, each with k classes1. Table 1 contains some highlights, with additional results and experimental details in the Appendix E. Here we show both the average accuracy on the final 20% of each data set, as well as the average run time for each algorithm. |
| Researcher Affiliation | Academia | Young Hun Jung Jack Goetz Department of Statistics University of Michigan Ann Arbor, MI 48109 EMAIL Ambuj Tewari |
| Pseudocode | Yes | Algorithm 1 Online Multiclass Boost-by-Majority (Online MBBM) ... Algorithm 2 Adaboost.OLM |
| Open Source Code | Yes | 1Codes are available at https://github.com/yhjung88/Online Boosting With VFDT |
| Open Datasets | Yes | We compare the new algorithms to existing ones for online boosting on several UCI data sets, each with k classes1. ... [22] C.L. Blake and C.J. Merz. UCI machine learning repository, 1998. URL http://archive.ics.uci. edu/ml. |
| Dataset Splits | No | The paper mentions evaluating on the 'final 20%' as a test set but does not explicitly describe train/validation/test splits, specific percentages for training/validation, or how the dataset was partitioned for these purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. It only mentions 'online decision trees fit using the VFDT algorithm'. |
| Software Dependencies | No | The paper mentions using the 'VFDT algorithm' but does not specify any software versions for libraries, frameworks, or programming languages (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper mentions using the 'VFDT algorithm' to fit '100 online decision trees' and trying 'five different values of the edge parameter γ' for Online MBBM. However, it does not provide specific hyperparameters like learning rates, batch sizes, epochs, or other detailed training configurations often found in experimental setup sections. |