Deep Boosting
Authors: Corinna Cortes, Mehryar Mohri, Umar Syed
ICML 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report the results of several experiments showing that its performance compares favorably to that of Ada Boost and Logistic Regression and their L1-regularized variants. and 4. Experiments |
| Researcher Affiliation | Collaboration | Corinna Cortes EMAIL Google Research, 111 8th Avenue, New York, NY 10011 Mehryar Mohri EMAIL Courant Institute and Google Research, 251 Mercer Street, New York, NY 10012 Umar Syed EMAIL Google Research, 111 8th Avenue, New York, NY 10011 |
| Pseudocode | Yes | Figure 2. Pseudocode of the Deep Boost algorithm for both the exponential loss and the logistic loss. |
| Open Source Code | No | No explicit statement about open-source code release or a link to a repository is found. |
| Open Datasets | Yes | We tested Deep Boost on the same UCI datasets used by these authors, http:// archive.ics.uci.edu/ml/datasets.html, specifically breastcancer, ionosphere, german(numeric) and diabetes. We also experimented with two optical character recognition datasets used by Reyzin & Schapire (2006), ocr17 and ocr49, which contain the handwritten digits 1 and 7, and 4 and 9 (respectively). Finally, because these OCR datasets are fairly small, we also constructed the analogous datasets from all of MNIST, http://yann. lecun.com/exdb/mnist/, which we call ocr17-mnist and ocr49-mnist. |
| Dataset Splits | Yes | Each dataset was randomly partitioned into 10 folds, and each algorithm was run 10 times, with a different assignment of folds to the training set, validation set and test set for each run. Specifically, for each run i 2 {0, . . . , 9}, fold i was used for testing, fold i + 1 (mod 10) was used for validation, and the remaining folds were used for training. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | In all of our experiments, the number of iterations was set to 100. and For Ada Boost-L1, we optimized over β 2 {2 i : i = 6, . . . , 0} and for Deep Boost, we optimized over β in the same range and λ 2 {0.0001, 0.005, 0.01, 0.05, 0.1, 0.5}. and Specifically, for Ada Boost we optimized over K 2 {1, . . . , 6}, for Ada Boost-L1 we optimized over those same values for K and β 2 {10 i : i = 3, . . . , 7}, and for Deep Boost we optimized over those same values for K, β and λ 2 {10 i : i = 3, . . . , 7}. |