Logarithmic Time One-Against-Some
Authors: Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Empirical Results We study several questions empirically. ... Throughout this section we conduct experiments using learning with a linear representation. |
| Researcher Affiliation | Collaboration | 1University of Maryland 2Microsoft. Correspondence to: Paul Mineiro <pmineiro@microsoft.com>. |
| Pseudocode | Yes | Algorithm 1 Predict. ... Algorithm 2 Train. ... Algorithm 3 update router. ... Algorithm 4 update regressors |
| Open Source Code | Yes | Implementations of the learning algorithms, and scripts to reproduce the data sets and experimental results, are available on github (Mineiro, 2017). ... Mineiro, Paul. Recall tree demo, 2017. URL https: //github.com/John Langford/vowpal_ wabbit/tree/master/demo/recall_tree. |
| Open Datasets | Yes | Table 1. Datasets used for experimentation. Dataset Source Task Classes Examples ALOI Geusebroek et al. (2005) Imagenet Oquab et al. (2014) LTCB Mahoney (2009) ODP Bennett & Nguyen (2009) |
| Dataset Splits | No | The paper mentions 'progressive validation loss' but does not provide explicit training/test/validation dataset splits for all experiments or a general splitting methodology for reproducibility. |
| Hardware Specification | No | The paper mentions 'GPUs' and '24 cores in parallel' but does not specify exact models of GPUs, CPUs, or other detailed hardware specifications for the experiments. |
| Software Dependencies | No | The paper mentions Vowpal Wabbit in the GitHub link but does not provide specific version numbers for software dependencies or other libraries used in the experiments. |
| Experiment Setup | Yes | Here, λ is a hyperparameter of the recall tree (in fact, it is the only additional hyperparameter), which controls how aggressively the tree branches." and "When F = O(log K) this does not compromise the goal of achieving logarithmic time classification." and "To test this we trained on the LTCB dataset with a multiplier on the bound of either 0 (i.e. just using empirical recall directly) or 1. |