Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
Authors: Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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... |