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..
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting
Authors: Oscar Beijbom, Mohammad Saberian, David Kriegman, Nuno Vasconcelos
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate (1) the importance of guess-aversion and (2) that the GLL loss function outperforms other loss functions for multiclass boosting. |
| Researcher Affiliation | Academia | University of California, San Diego, 9500 Gilman Drive, 92093 La Jolla, CA |
| Pseudocode | Yes | Algorithm 1 (GLL, GEL, Ls, Lt)-MCBoost |
| Open Source Code | Yes | The MATLAB implementation of the proposed boosting algorithms, along with experimental details is available in supplementary material1. |
| Open Datasets | Yes | These experiments used 10 UCI datasets and a large scale computer vision dataset for coral classification (Beijbom et al., 2012). |
| Dataset Splits | Yes | For these, training/testing partition are either predefined or the data is randomly split into 80% training and 20% testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB implementation' and 'LIBLINEAR implementation (Fan et al., 2008)' but does not provide specific version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | For each dataset, a random symmetric cost matrix was generated, Cj,k j = k drawn uniformly from [1, 10] R, and all boosted classifiers were trained with 100 iterations. The procedure was repeated 50 times per dataset... The boosting methods were trained with 500 iterations. |