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..
Learning to select for a predefined ranking
Authors: Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev, Pavel Serdyukov
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our of๏ฌine and online experiments with a large-scale product search engine demonstrate the overwhelming advantage of our methods over the baselines in terms of all key quality metrics. |
| Researcher Affiliation | Collaboration | 1Yandex, Moscow, Russia 2Skoltech University, Moscow, Russia 3Faculty of Computer Science, Higher School of Economics, Moscow, Russia 4Department of Innovation and High Technology, Moscow Institute of Physics and Technology, Dolgoprudny, Russia. |
| Pseudocode | No | The paper refers to 'Algorithm 3 in (Prokhorenkova et al., 2018)' but does not contain its own pseudocode or algorithm blocks. |
| Open Source Code | Yes | The whole source code of our learning algorithm and its difference from Cat Boost release 0.10.04 are available10. Later, it was added to Cat Boost as Stochastic Filter loss11. 10https://github.com/Take Over/catboost/tree/0.10.4_release 11https://github.com/catboost/catboost/commit/df18d16 |
| Open Datasets | Yes | The data with labels used for learning all the models and for evaluation by DCG-RR (see description of labels and metrics below) is available in open source 7. 7https://research.yandex.com/datasets/market |
| Dataset Splits | Yes | we randomly split all the queries from the collected dataset D into 5 parts of equal size to run 5-fold cross-validation: at each i of ๏ฌve runs, 80% of the queries (Di train) are used for training, 10% (Di valid) are used for tuning hyperparameters of the algorithms and 10% (Di test) are used for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | Yes | We use GBDT implementation in opensourced Cat Boost9 Python package... The whole source code of our learning algorithm and its difference from Cat Boost release 0.10.04 are available10. |
| Experiment Setup | No | See Section 4 of the supplementary for Cat Boost parameters we used. |