Deep Boosting
Authors: Corinna Cortes, Mehryar Mohri, Umar Syed
ICML 2014 | Conference 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 CORINNA@GOOGLE.COM Google Research, 111 8th Avenue, New York, NY 10011 Mehryar Mohri MOHRI@CIMS.NYU.EDU Courant Institute and Google Research, 251 Mercer Street, New York, NY 10012 Umar Syed USYED@GOOGLE.COM 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}. |