Exact Exponent in Optimal Rates for Crowdsourcing
Authors: Chao Gao, Yu Lu, Dengyong Zhou
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Under the classic Dawid-Skene model, we establish matching upper and lower bounds with an exact exponent m I(π) in which m is the number of workers and I(π) the average Chernoff information that characterizes the workers collective ability. Such an exact characterization of the error exponent allows us to state a precise sample size requirement m > 1 I(π) log 1 ϵ in order to achieve an ϵ misclassification error. In addition, our results imply the optimality of various EM algorithms for crowdsourcing initialized by consistent estimators. The main focus of this paper is to find the exact error exponent to better guide algorithm design and optimization. |
| Researcher Affiliation | Collaboration | Chao Gao CHAO.GAO@YALE.EDU Yale University, 24 Hillhouse Ave, New Haven, CT 06511 USA Yu Lu YU.LU@YALE.EDU Yale University, 24 Hillhouse Ave, New Haven, CT 06511 USA Dengyong Zhou DENGYONG.ZHOU@MICROSOFT.COM Microsoft Research, One Microsoft Way, Redmond, WA 98052 USA |
| Pseudocode | No | The paper describes algorithmic steps using equations (e.g., (7), (8), (10), (11), (12)) but does not include formal pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on a specific publicly available dataset. It models a general crowdsourcing scenario. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that require training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments run on specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers that would be needed for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |