Addressing Bias in Online Selection with Limited Budget of Comparisons
Authors: Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we confirm our theoretical findings via numerical experiments, and we give further insight regarding the behavior of the algorithms we presented and how they compare to each other. In all the empirical experiments of this section, each point is computed over 106 independent trials. |
| Researcher Affiliation | Collaboration | Ziyad Benomar ENSAE, Ecole Polytechnique, Fair Play joint team EMAIL Evgenii Chzhen CNRS, LMO, Université Paris-Saclay EMAIL Nicolas Schreuder CNRS, Laboratoire d informatique Gaspard Monge (LIGM/UMR 8049) EMAIL Vianney Perchet CREST, ENSAE, Criteo AI LAB Fairplay joint team EMAIL |
| Pseudocode | Yes | A formal description is given in Algorithm 1, and a visual representation for the case of three groups is provided in Figure 1. Algorithm 1: Dynamic-Threshold algorithm AB (αk,b)k [K],b B |
| Open Source Code | Yes | The code used for the experiments is available at github.com/Ziyad-Benomar/Addressing-bias-in-online-selection-with-limited-budget-of-comparisons. |
| Open Datasets | No | The paper does not use external public datasets. It simulates a theoretical problem with parameters (N, λk) without relying on pre-existing datasets. |
| Dataset Splits | No | The paper describes numerical experiments for theoretical models and does not involve typical dataset splits (training, validation, test) found in empirical machine learning studies. |
| Hardware Specification | No | The paper does not specify the type of hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the numerical experiments. |
| Software Dependencies | No | The paper provides a link to the code repository but does not explicitly list specific software dependencies with version numbers (e.g., programming language, libraries, or solvers). |
| Experiment Setup | Yes | In this section, we confirm our theoretical findings via numerical experiments, and we give further insight regarding the behavior of the algorithms we presented and how they compare to each other. In all the empirical experiments of this section, each point is computed over 106 independent trials. ... for N = 500, λ = 0.7, for B {0, 1, 2} ... |