Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
Authors: Christopher Tosh, Sanjoy Dasgupta
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents NP-hardness and hardness of approximation results for maximum likelihood estimation of mixtures of spherical Gaussians. |
| Researcher Affiliation | Academia | Christopher Tosh EMAIL Sanjoy Dasgupta EMAIL Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0404, USA |
| Pseudocode | No | The paper describes theoretical results and proofs, and does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper refers to abstract 'data points x1, . . . , xn' and 'data set X' for theoretical analysis, but does not provide any concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with specific datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | This paper presents theoretical NP-hardness results and does not describe any experiments requiring specific hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on NP-hardness proofs, therefore it does not contain any specific experimental setup details, hyperparameters, or training configurations. |