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..
Multi-Class Deep Boosting
Authors: Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we report the results of experiments demonstrating that multi-class Deep Boost outperforms Ada Boost.MR and multinomial (additive) logistic regression, as well as their L1-norm regularized variants, on several datasets. |
| Researcher Affiliation | Collaboration | Vitaly Kuznetsov Courant Institute 251 Mercer Street New York, NY 10012 EMAIL Mehryar Mohri Courant Institute & Google Research 251 Mercer Street New York, NY 10012 EMAIL Umar Syed Google Research 76 Ninth Avenue New York, NY 10011 EMAIL |
| Pseudocode | Yes | Figure 1: Pseudocode of the MDeep Boost Sum algorithm for both the exponential loss and the logistic loss. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In our experiments, we used 8 UCI data sets: abalone, handwritten, letters, pageblocks, pendigits, satimage, statlog and yeast see more details on these datasets in Table 4, Appendix L. |
| Dataset Splits | Yes | To set these parameters, we used the following parameter optimization procedure: we randomly partitioned each dataset into 4 folds and, for each tuple (λ, β, K) in the set of possible parameters (described below), we ran MDeep Boost Sum, with a different assignment of folds to the training set, validation set and test set for each run. Specifically, for each run i {0, 1, 2, 3}, fold i was used for testing, fold i + 1 (mod 4) was used for validation, and the remaining folds were used for training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For each dataset, the set of possible values for λ and β was initialized to {10 5, 10 6, . . . , 10 10}, and to {1, 2, 3, 4, 5} for the maximum tree depth K. |