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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Information Elicitation Mechanisms for Statistical Estimation
Authors: Yuqing Kong, Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu2095-2102
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study learning statistical properties from strategic agents with private information. In this problem, agents must be incentivized to truthfully reveal their information even when it cannot be directly veri๏ฌed. Moreover, the information reported by the agents must be aggregated into a statistical estimate. We study two fundamental statistical properties: estimating the mean of an unknown Gaussian, and linear regression with Gaussian error. The information of each agent is one point in a Euclidean space. Our main results are two mechanisms for each of these problems which optimally aggregate the information of agents in the truth-telling equilibrium. |
| Researcher Affiliation | Academia | Yuqing Kong,1 Grant Schoenebeck,2 Biaoshuai Tao,2 Fang-Yi Yu2 1CFCS, Computer Science Dept., Peking University, 2University of Michigan, Ann Arbor EMAIL, EMAIL |
| Pseudocode | Yes | Mechanism 1 The metric mechanism Mmetric [...] Mechanism 2 Proxy-BTS mechanism Mproxy [...] Mechanism 3 The disagreement mechanism Mdisagree |
| Open Source Code | No | The paper does not contain any statements about making source code available or links to code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on mechanism design and proofs, not on empirical studies or the use of datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes theoretical mechanisms and provides mathematical proofs; it does not report on any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper describes theoretical mechanisms and provides mathematical proofs; it does not report on any empirical experiments that would require software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus it does not describe an experimental setup with hyperparameters or system-level training settings. |