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].
Mode Connectivity in Auction Design
Authors: Christoph Hertrich, Yixin Tao, László A. Végh
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that they satisfy mode connectivity, i.e., locally optimal solutions are connected by a simple, piecewise linear path such that every solution on the path is almost as good as one of the two local optima. Mode connectivity has been recently investigated as an intriguing empirical and theoretically justifiable property of neural networks used for prediction problems. Our results give the first such analysis in the context of differentiable economics, where neural networks are used directly for solving non-convex optimization problems. |
| Researcher Affiliation | Academia | Christoph Hertrich Department of Mathematics London School of Economics and Political Science, UK EMAIL Yixin Tao ITCS, Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics, China EMAIL László A. Végh Department of Mathematics London School of Economics and Political Science, UK EMAIL |
| Pseudocode | No | The paper describes theoretical proofs and concepts, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper refers to theoretical distributions and samples from them ('samples from the distribution F'), but does not provide access information (link, DOI, citation) for a specific public dataset used for experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not provide specific details about an experimental setup, such as hyperparameters or training configurations. |