Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Mechanism Design for Information Acquisition
Authors: Federico Cacciamani, Matteo Castiglioni, Nicola Gatti
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 30,222 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,667 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we provide an algorithm that efficiently computes an optimal incentive compatible (IC) mechanism. Then, we focus on the online problem... For the full feedback problem, we propose an algorithm that guarantees O(T) regret and violation, while for the bandit feedback setting we present an algorithm that attains O(T α) regret and O(T 1 α/2) violation for any α [1/2, 1]. Finally, we complement our results providing a tight lower bound. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. Correspondence to: Federico Cacciamani <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Algorithm for the full feedback setting |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the release of source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of empirical datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments requiring training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |