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..
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
Authors: Sonja Kraiczy, Edith Elkind
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the dependence of the sample size on p, and to see how well the lower bound in Proposition 3.9 performs, we ran experiments with m = 1000 alternatives (we choose a relatively large value of m for robustness; as argued above the results are likely to be very similar for other values of m), varying p as p {0.001, 0.002, . . . , 0.5}. Figure 1 shows the average sample size over 1000 repetitions needed to identify the underlying axis from Up( ) depending on p(1 p) (red), together with a plot of p(1 p) (blue), in log-log scale. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, Oxford, United Kingdom. Correspondence to: Edith Elkind <EMAIL>. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes experiments involving sampling from theoretical distributions to verify bounds, not training models on traditional datasets or providing access to such datasets. |
| Dataset Splits | No | The paper does not specify any training/validation/test dataset splits. The experiments are simulations to verify theoretical bounds on sample size. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific names or version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | To test the dependence of the sample size on p, and to see how well the lower bound in Proposition 3.9 performs, we ran experiments with m = 1000 alternatives (we choose a relatively large value of m for robustness; as argued above the results are likely to be very similar for other values of m), varying p as p {0.001, 0.002, . . . , 0.5}. Figure 1 shows the average sample size over 1000 repetitions needed to identify the underlying axis from Up( ) depending on p(1 p) (red), together with a plot of p(1 p) (blue), in log-log scale. |