Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Logarithmic Time One-Against-Some
Authors: Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | Venue PDF | 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 <EMAIL>. |
| 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. |