Fair Online Bilateral Trade
Authors: François Bachoc, Nicolò Cesa-Bianchi, Tom Cesari, Roberto Colomboni
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We conclude by providing tight regret bounds when, after each interaction, the platform is allowed to observe the true traders valuations. and We present our main contribution: a complete characterization of the regret regimes for fair gain from trade... and Theorem 2. In the stochastic case, under the additional assumption that for each t N the seller s valuation St is independent of the buyer s valuation Bt, by setting K = T 2/3 , the regret suffered by Algorithm 1 is O(T 2/3). |
| Researcher Affiliation | Academia | François Bachoc IMT Université Paul Sabatier Toulouse, France, Nicolò Cesa-Bianchi Department of Computer Science Università degli Studi di Milano DEIB Politecnico di Milano Milano, Italy, Tommaso Cesari EECS University of Ottawa Ottawa, Canada, Roberto Colomboni DEIB Politecnico di Milano Department of Computer Science Università degli Studi di Milano Milano, Italy |
| Pseudocode | Yes | Algorithm 1: Convolution Pricing (Stochastic Setting), Algorithm 2: Follow the Best Empirical Price, Algorithm 3: Double Binary Search Pricing |
| Open Source Code | No | The paper focuses on theoretical contributions and does not mention the release of source code for the described methodologies, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper is a theoretical work that analyzes algorithms and proves regret bounds. It does not utilize or reference any specific, publicly available datasets for empirical validation, instead relying on theoretical models of valuation distributions (e.g., i.i.d. processes). |
| Dataset Splits | No | This paper is a theoretical work focusing on algorithm design and mathematical proofs. It does not involve empirical experiments with datasets, and therefore, no training, validation, or test dataset splits are mentioned. |
| Hardware Specification | No | The paper is a theoretical work focused on algorithm design and mathematical analysis; it does not report on any experiments and thus provides no information on hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical analysis of algorithms. It does not describe any experimental setup or implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper focuses on theoretical contributions, including algorithm design and regret analysis, and does not include an empirical experimental section. Therefore, it does not describe specific experimental setup details such as hyperparameters or training configurations. |