Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
Authors: Yihan Zhang, Nir Weinberger
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical validation of the performance of the estimator θˆcov(Xn 1 ; k) is shown in Appendix F. Numerical validation of the performance of the estimator δˆcorr(Xn 1 ; θ ) = 1 2(1 ρˆcorr(Xn 1 ; θ )) in the mismatched (Theorem 4) and matched (Corollary 5) cases is provided in Appendix F. Numerical validation of the performance of Algorithm 1 can be found in Appendix F. |
| Researcher Affiliation | Academia | Yihan Zhang Institute of Science and Technology, Austria zephyr.z798@gmail.com Nir Weinberger Technion Israel Institute of Technology nirwein@technion.ac.il |
| Pseudocode | Yes | Algorithm 1 Mean estimation for unknown δ |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper describes a theoretical model that generates 'n samples'. It does not use or provide access information for a pre-existing publicly available or open dataset for training. |
| Dataset Splits | No | The paper is theoretical with numerical validations mentioned, but it does not specify explicit training/validation/test dataset splits. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Hardware Specification | No | The paper does not provide specific hardware details. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The paper does not provide specific software dependency versions. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |