Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Addressing Bias in Online Selection with Limited Budget of Comparisons
Authors: Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 43,519 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,663 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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} ... |