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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
As You Like It: Localization via Paired Comparisons
Authors: Andrew K. Massimino, Mark A. Davenport
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we perform a range of synthetic experiments to demonstrate our approach. (Section 6). The paper also includes figures (e.g., Figure 2: Mean error norm x x as 𝜎2 varies...) showing experimental results from simulations. |
| Researcher Affiliation | Academia | Andrew K. Massimino EMAIL Mark A. Davenport EMAIL School of Electrical and Computer Engineering Georgia Institute of Technology 777 Atlantic Dr NW Atlanta, GA 30332 USA |
| Pseudocode | No | The paper describes mathematical formulations for optimization problems (25) and (26) in Section 5, and a linearization procedure for solving a non-convex problem, but does not present any of these as structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any statement about releasing code, a link to a code repository, or mention of code in supplementary materials. |
| Open Datasets | No | The paper exclusively uses synthetic data generated for its experiments. For example, "In Fig. 2, we let x R{2,3,4,8} with x = R= 1. We vary 𝜎2 and perform 1500 trials, each with 𝑚= 100 or 𝑚= 200 pairs of points drawn according to 𝒩(0, 𝜎2𝐼)." (Section 6.1). No public datasets are mentioned or accessed. |
| Dataset Splits | No | The paper conducts simulations with synthetic data and mentions "trials" but not typical training/test/validation splits for a fixed dataset. For example, "1500 trials" (Section 6.1), "100 independent trials" (Section 6.2). These refer to experimental runs with newly generated data for each trial, not predefined splits of a static dataset. |
| Hardware Specification | No | The paper's "Simulations" section discusses experimental results but does not specify any hardware components (e.g., GPU models, CPU types, memory) used to run these simulations. |
| Software Dependencies | No | The paper does not mention any specific software libraries, frameworks, or programming languages with version numbers used for the implementation of the algorithms or simulations. |
| Experiment Setup | Yes | The paper provides several experimental setup details, such as: "In Fig. 2, we let x R{2,3,4,8} with x = R= 1. We vary 𝜎2 and perform 1500 trials, each with 𝑚= 100 or 𝑚= 200 pairs of points drawn according to 𝒩(0, 𝜎2𝐼)." (Section 6.1). In Section 6.2: "we set 𝑛= 5 and generate 𝑚= 1000 pairs of points and a random x with x = 0.7." Also, "As a rule of thumb, we set 𝜈= 2𝜅." and "we set w(𝑘+1) 𝜒 w(𝑘) + (1 𝜒) w(𝑘) with 𝜒= 0.7." |