Logarithmic Time Online Multiclass prediction
Authors: Anna E. Choromanska, John Langford
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We address several hypotheses experimentally. ... In Table 2 and 3 we report respectively train time and per-example test time ... Table 4: Test error (%) and confidence interval on all problems. |
| Researcher Affiliation | Collaboration | Anna Choromanska Courant Institute of Mathematical Sciences New York, NY, USA achoroma@cims.nyu.edu John Langford Microsoft Research New York, NY, USA jcl@microsoft.com |
| Pseudocode | Yes | Algorithm 1 LOMtree algorithm (online tree training) |
| Open Source Code | No | The paper states 'All methods were implemented in the Vowpal Wabbit [25] learning system', referring to an external open-source project. However, it does not explicitly state that the LOMtree algorithm's specific implementation described in this paper is open-source or provide a link to its code. |
| Open Datasets | Yes | We conducted experiments on a variety of benchmark multiclass datasets: Isolet, Sector, Aloi, Image Net (Im Net) and ODP13. The details of the datasets are provided in Table 1. ... [27] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. |
| Dataset Splits | Yes | The datasets were divided into training (90%) and testing (10%). Furthermore, 10% of the training dataset was used as a validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory specifications, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper states 'All methods were implemented in the Vowpal Wabbit [25] learning system', but does not provide specific version numbers for Vowpal Wabbit or any other software dependencies. |
| Experiment Setup | Yes | The regressors in the tree nodes for LOMtree, Rtree, and Filter tree as well as the OAA regressors were trained by online gradient descent for which we explored step sizes chosen from the set {0.25, 0.5, 0.75, 1, 2, 4, 8}. We used linear regressors. For each method we investigated training with up to 20 passes through the data and we selected the best setting of the parameters (step size and number of passes) as the one minimizing the validation error. Additionally, for the LOMtree we investigated different settings of the stopping criterion for the tree expansion: T = {k 1, 2k 1, 4k 1, 8k 1, 16k 1, 32k 1, 64k 1}, and swap resistance RS = {4, 8, 16, 32, 64, 128, 256}. |