UniRank: Unimodal Bandit Algorithms for Online Ranking
Authors: Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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.' |