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..
Contextual bandits with concave rewards, and an application to fair ranking
Authors: Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the empirical performance of our algorithm to relevant baselines on a music recommendation task. We present two experimental evaluations of our approach, which are fully detailed in App. B. |
| Researcher Affiliation | Collaboration | Virginie Do PSL University & Meta AI EMAIL Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier Meta AI EMAIL |
| Pseudocode | Yes | Algorithm 1: FW-Lin UCBRank: linear contextual bandits for fair ranking. Algorithm 2: Generic Frank-Wolfe algorithm for CBCR. Algorithm 3: FW-lin UCB: linear CBCR with K arms. Algorithm 4: FW-Square CB: contextual bandits with concave rewards and regression oracles Algorithm 5: FW-lin UCBRank: linear contextual bandits for fair ranking. |
| Open Source Code | No | The paper does not contain an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | Following (Patro et al., 2020), we use the Last.fm music dataset from (Cantador et al., 2011) |
| Dataset Splits | No | The paper mentions generating contexts and rewards from a dataset, and sets parameters like 'k = 10' for ranking slots, but does not provide explicit train/validation/test dataset splits, percentages, or methodology for splitting the data used in experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | Our experiments are fully implemented in Python 3.9. Using the Python library Implicit, MIT License: https://implicit.readthedocs.io/. |
| Experiment Setup | Yes | For all algorithms, the regularization parameter of the Ridge regression is set to λ = 0.1. We set β = 0.5 for all objectives and for welf, we set α = 0.5. We use β0 = 0.01. We choose d = 10 in the data generation and λ = 0.1 in the Ridge regression. |