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..
Gradient Boosted Decision Trees for High Dimensional Sparse Output
Authors: Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply our algorithm to extreme multilabel classification problems, and show that the proposed GBDT-SPARSE achieves an order of magnitude improvements in model size and prediction time over existing methods, while yielding similar performance. |
| Researcher Affiliation | Collaboration | 1Google Research, Mountain View, USA 2University of California at Davis, Davis, USA 3Microsoft, Mountain View, USA 4Facebook, Menlo Park, USA 5University of Texas at Austin, Austin, USA. |
| Pseudocode | Yes | Algorithm 1: GBDT-SPARSE tree node splitting algorithm |
| Open Source Code | No | The paper refers to a link for Light GBM, a baseline method ('https://github.com/Microsoft/LightGBM'), but does not provide a link or statement about the availability of their own GBDT-SPARSE code. |
| Open Datasets | Yes | Data: We conducted experiments on 5 standard and publicly available multi-label learning datasets.3 Table 2 shows the associated details. NUS-WIDE is available at http://lms.comp.nus.edu.sg/ research/NUS-WIDE.htm. All other datasets are available at http://manikvarma.org/downloads/XC/XMLRepository.html. |
| Dataset Splits | No | The paper mentions 'training samples' and 'testing samples' in Table 2, but does not explicitly define a 'validation' dataset split or describe the methodology for creating such a split. |
| Hardware Specification | Yes | All experiments are conducted on a machine with an Intel Xeon X5440 2.83GHz CPU and 32GB RAM. For PD-Sparse we use a similar machine with 192GB memory due to its large memory footprint. We run our algorithm with Delicious-200K on a 28-core dual socket E5-2683v3 machine |
| Software Dependencies | No | The paper mentions various baselines like XGBoost, Light GBM, LEML, FASTXML, SLEEC, and PD-SPARSE, stating they used 'their highly optimized C++ implementation published along with the original papers,' but does not provide specific version numbers for any software, including their own. |
| Experiment Setup | Yes | For our method, we kept most of the parameters fixed for all the datasets: hmax = 10, nleaf = 100, and, λ = 5, where hmax and nleaf are the maximum level of the tree and the minimal number of data points in each leaf. Leaf node sparsity k was set to 100 for Delicious-200K and 20 for all others. This parameter can be very intuitively set as an increasing function of label set size. We hand tuned the projection dimensionality d and set it to 100 for Delicious and Wiki10-31K, and 50 for others. |