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..
Online Information Acquisition: Hiring Multiple Agents
Authors: Federico Cacciamani, Matteo Castiglioni, Nicola Gatti
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we design a polynomial-time algorithm to find an optimal incentive compatible mechanism. Then, we study an online problem, where the principal repeatedly interacts with a group of unknown agents. We design a no-regret algorithm that provides e O(T 2/3) regret with respect to an optimal mechanism, matching the state-of-the-art bound for single-agent settings. |
| Researcher Affiliation | Academia | Federico Cacciamani Politecnico di Milano EMAIL Matteo Castiglioni Politecnico di Milano EMAIL Nicola Gatti Politecnico di Milano EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Information Acquisition; Algorithm 2 ESTIMATEPROB; Algorithm 3 COMMIT; Algorithm 4 ESTIMATECOSTS; Algorithm 5 Binary Search (BS) |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any datasets for training or experimentation, hence no information about public availability of a dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets, so it does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training settings. |