Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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 and potential bias; scores should be interpreted as estimates.
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
Authors: Shivam Gupta, Jasper C.H. Lee, Eric Price
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 35,092 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,745 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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). |