High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
Authors: Shivam Gupta, Jasper C.H. Lee, Eric Price
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experimental validation on a synthetic high-dimensional example. and Figure 3. Error scaled by N for different algorithms, for a synthetic Gaussian mixture. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin 2Department of Computer Sciences and Institute for Foundations of Data Science, University of Wisconsin-Madison. |
| Pseudocode | Yes | Algorithm 1 Local smoothed MLE for one dimension, Algorithm 2 Global smoothed MLE for one dimension, Algorithm 3 High-dimensional Local MLE, Algorithm 4 High-dimensional Global MLE |
| Open Source Code | Yes | Our implementation is available here: https://github.com/shivamgupta2/High-dimensional-location |
| Open Datasets | No | The paper uses a synthetic dataset described as 'a mixture of three gaussians' which is generated for the experiments, with no concrete access information provided. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | We consider three algorithms: our algorithm with smoothing radius 0.1;... For our algorithm with R = 0.01I... Newton s method (i.e., our algorithm except with R = 0 and multiple steps; we use 10 steps). |