Minimax-Optimal Location Estimation
Authors: Shivam Gupta, Jasper Lee, Eric Price, Paul Valiant
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare various location estimation methods on synthetic data from a fairly simple, but irregular, piecewise linear distribution (Figure 3(a)). We set n = 10 and aim for 90% confidence intervals. In Figure 3(b), we plot the CDF of the point error produced by the MLE, the 0.3-smoothed MLE, and our two algorithms (Algorithm 1 and Algorithm 4). |
| Researcher Affiliation | Academia | Shivam Gupta The University of Texas at Austin shivamgupta@utexas.edu Jasper C.H. Lee University of Wisconsin Madison jasper.lee@wisc.edu Eric Price The University of Texas at Austin ecprice@cs.utexas.edu Paul Valiant Purdue University pvaliant@gmail.com |
| Pseudocode | Yes | Algorithm 1 The algorithm At for a fixed estimation accuracy ϵ [...] Algorithm 2. Consider the optimal failure probability δ as a function of the estimation accuracy ϵ. [...] Algorithm 3 Estimator minimizing RA cos ρ + δA sin ρ for a given angle ρ [0, π/2] [...] Algorithm 4 (Minimax-optimal confidence-interval estimator). [...] Algorithm 5 Approximately computing ϵ from δ using binary search [...] Algorithm 6 Binary search for the optimal estimator through the slope angle ρ |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories. |
| Open Datasets | No | We compare various location estimation methods on synthetic data from a fairly simple, but irregular, piecewise linear distribution (Figure 3(a)). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits or mention cross-validation. It uses 'synthetic data' for evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We set n = 10 and aim for 90% confidence intervals. |