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..
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]' |