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..
UniRank: Unimodal Bandit Algorithms for Online Ranking
Authors: Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare Uni Rank to Top Rank (Lattimore et al., 2018), PB-MHB (Gauthier et al., 2021a), GRAB (Gauthier et al., 2021b), and Cascade KL-UCB (Kveton et al., 2015a). The experiments are conducted on the KDD Cup 2012 track 2 dataset, on the Yandex dataset (Yandex, 2013), and on a model with artificial parameters. |
| Researcher Affiliation | Collaboration | 1Louis Vuitton, F-75001 Paris, France 2IRISA UMR 6074 / INRIA rba, F-35000 Rennes, France 3Univ Rennes, Ensai, CNRS, CREST UMR 9194, F-35000 Rennes, France 4Univ. Rennes 1, F-35000 Rennes, France 5 Institut Universitaire de France, M.E.S.R.I., F-75231 Paris. |
| Pseudocode | Yes | Algorithm 1 Uni Rank: Unimodal Bandit Algorithm for Online Ranking |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The experiments are conducted on the KDD Cup 2012 track 2 dataset, on the Yandex dataset (Yandex, 2013), and on a model with artificial parameters. |
| Dataset Splits | No | The paper mentions parameters for simulations and item quantities (L, K) for datasets but does not specify explicit training, validation, and test splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper states: 'We run our experiments on an internal cluster'. This is a general statement and does not provide specific hardware details (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions 'We use the GPL3 Pyclick library (Chuklin et al., 2015)' but does not provide a version number for this library or any other software dependencies. |
| Experiment Setup | No | The paper specifies parameters for the simulated models (e.g., L, K, θ, κ values) but does not provide details on the experimental setup for Uni Rank itself, such as learning rates, batch sizes, optimizers, or training schedules. It mentions tuning PB-MHB but defers to another paper for the values: 'To tune PB-MHB, we use the values recommended by (Gauthier et al., 2021a) for these datasets.' |