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..
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
Authors: Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted an experiment with six arms, where the rewards followed the order (µ1 > µ2 > µ3 > µ4 > µ5 > µ6). The experiment spanned a horizon of 104 time steps and was averaged over 100 epochs. |
| Researcher Affiliation | Collaboration | Ahmed Ben Yahmed CREST, ENSAE, Palaiseau, France Criteo AI Lab, Paris, France... Clément Calauzènes Criteo AI Lab, Paris, France... Vianney Perchet CREST, ENSAE, Palaiseau, France Criteo AI Lab, Paris, France |
| Pseudocode | Yes | Algorithm 1: Strategic Successive Elimination (S-SE) |
| Open Source Code | No | The paper does not provide explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The empirical analysis section states 'We conducted an experiment with six arms, where the rewards followed the order (µ1 > µ2 > µ3 > µ4 > µ5 > µ6). The experiment spanned a horizon of 104 time steps and was averaged over 100 epochs.' This refers to simulated data and does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes experiments on simulated data but does not provide specific train/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We conducted an experiment with six arms, where the rewards followed the order (µ1 > µ2 > µ3 > µ4 > µ5 > µ6). The experiment spanned a horizon of 104 time steps and was averaged over 100 epochs. Our study examined three specific scenarios: 1. Untruthful Arbitrary Reporting... 2. Truthful Reporting... 3. 'Optimal' Reporting... |