Notice: 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. Full accuracy metrics and methodology are described in [1].
Minimax-Optimal Location Estimation
Authors: Shivam Gupta, Jasper Lee, Eric Price, Paul Valiant
NeurIPS 2023 | Venue PDF | 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 EMAIL Jasper C.H. Lee University of Wisconsin Madison EMAIL Eric Price The University of Texas at Austin EMAIL Paul Valiant Purdue University EMAIL |
| 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. |