Contextual Bandits with Knapsacks for a Conversion Model
Authors: Zhen LI, Gilles Stoltz
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A simulation study on partially simulated but realistic data may be found in Appendix F. The underlying dataset is the standard default of credit card clients dataset of UCI [2016], initially provided by Yeh and Lien [2009]. |
| Researcher Affiliation | Collaboration | Zhen Li BNP Paribas, 16 boulevard des Italiens, 75009 Paris, France zhen.li@bnpparibas.com Gilles Stoltz Université Paris-Saclay, CNRS, Laboratoire de mathématiques d Orsay, 91405, Orsay, France gilles.stoltz@universite-paris-saclay.fr HEC Paris, 1 rue de la Libération, 78350 Jouy-en-Josas, France stoltz@hec.fr |
| Pseudocode | Yes | BOX B: LOGISTIC-UCB1 FOR DIRECT SOLUTIONS TO OPT PROBLEMS |
| Open Source Code | Yes | (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The underlying dataset is the standard default of credit card clients dataset of UCI [2016], initially provided by Yeh and Lien [2009]. (It may be used under a Creative Commons Attribution 4.0 International [CC BY 4.0] license.) |
| Dataset Splits | No | The paper mentions a simulation study using the UCI dataset in Appendix F, and the authors state in the ethics checklist (3.b) that they specified training details including data splits. However, Appendix F itself does not explicitly provide percentages, sample counts, or specific methods for train/validation/test splits. |
| Hardware Specification | Yes | We ran our simulations on a machine with an Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz processor, and with 16GB of RAM. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., programming languages, libraries, frameworks, or solvers used in the implementation). |
| Experiment Setup | Yes | Parameters: regularization parameter λ > 0; conservative-budget parameter BT; upperconfidence bonuses εs(a, x) > 0, for s ≥ 1 and (a, x) ∈ A {anull} × X. ... We set a confidence level 1 − δ ∈ (0, 1) and use parameters λ = m ln(1 + T/m), 2T ln(4d/δ) , and εt(a, x) stated in (9) of the supplementary material. |