Boosting with Abstention
Authors: Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report the results of several experiments suggesting that our algorithm provides a significant improvement in practice over two confidence-based algorithms. |
| Researcher Affiliation | Collaboration | Corinna Cortes Google Research New York, NY 10011 corinna@google.com Giulia De Salvo Courant Institute New York, NY 10012 desalvo@cims.nyu.edu Mehryar Mohri Courant Institute and Google New York, NY 10012 mohri@cims.nyu.edu |
| Pseudocode | Yes | Figure 3: Pseudocode of the BA algorithm for both the exponential loss with Φ1(u) = Φ2(u) = exp(u) as well as for the logistic loss with Φ1(u) = Φ2(u) = log2(1 + eu). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We tested the algorithms on six data sets from UCI s data repository, specifically australian, cod, skin, banknote, haberman, and pima. For more information about the data sets, see Appendix I. For each data set, we implemented the standard 5-fold cross-validation where we randomly divided the data into training, validation and test set with the ratio 3:1:1. [...] We have also successfully run BA on the CIFAR-10 data set (boat and horse images) which contains 10,000 instances and we believe that our algorithm can scale to much larger datasets. |
| Dataset Splits | Yes | For each data set, we implemented the standard 5-fold cross-validation where we randomly divided the data into training, validation and test set with the ratio 3:1:1. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions "implemented in CVX [8]" and cites "Scikit-learn" but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | For all three algorithms, the cost values ranged over c 2 {0.05, 0.1, . . . , 0.5} while threshold γ ranged over γ 2 {0.08, 0.16, . . . , 0.96}. For the BA algorithm, the β regularization parameter ranged over β 2 {0, 0.05, . . . , 0.95}. All experiments for BA were based on T = 200 boosting rounds. |