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 [1].
ROI Maximization in Stochastic Online Decision-Making
Authors: Nicolò Cesa-Bianchi, Tom Cesari, Yishay Mansour, Vianney Perchet
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a novel theoretical framework for Return On Investment (ROI) maximization in repeated decision-making. Our algorithm provably converges to an optimal policy in class Π at a rate of order min{1/(N^2), N^1/3}, where N is the number of innovations and is the suboptimality gap in Π. |
| Researcher Affiliation | Collaboration | Nicolò Cesa-Bianchi Università degli Studi di Milano & DSRC EMAIL Tommaso Cesari Toulouse School of Economics EMAIL Yishay Mansour Tel Aviv University & Google research EMAIL Vianney Perchet CREST, ENSAE & Criteo AI Lab EMAIL |
| Pseudocode | Yes | Algorithm 1: Capped Policy Elimination (CAPE) and Algorithm 2: Extension to Countable (ESC) are clearly presented as pseudocode blocks. |
| Open Source Code | No | The paper does not provide open-source code. The checklist states: 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]' |
| Open Datasets | No | The paper is theoretical and does not involve training on a dataset. The checklist states: 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]' |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. The checklist states: 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]' |
| Hardware Specification | No | The paper is theoretical and does not involve experiments requiring hardware specifications. The checklist states: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]' |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. The checklist states: 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]' |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup with hyperparameters or training settings. The checklist states: 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]' |