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..
Contiguous Cake Cutting: Hardness Results and Approximation Algorithms
Authors: Paul W. Goldberg, Alexandros Hollender, Warut Suksompong1990-1997
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fair allocation of a cake... While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems... In addition, we consider a discretized setting... and show a number of hardness results strengthening those from prior work. |
| Researcher Affiliation | Academia | Paul W. Goldberg, Alexandros Hollender, Warut Suksompong Department of Computer Science University of Oxford |
| Pseudocode | Yes | Algorithm 1 1/3-Envy-Free Algorithm for Arbitrary Valuations; Algorithm 2 1/4-Envy-Free Algorithm for Uniform Single-Interval Valuations |
| Open Source Code | No | The paper does not provide any explicit statements or links about open-sourcing the code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and hardness proofs; it does not involve empirical training on datasets. Therefore, there is no information about public datasets for training. |
| Dataset Splits | No | This is a theoretical paper presenting algorithms and hardness proofs, not empirical experiments. Therefore, there is no information about dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper presenting algorithms and hardness proofs, not empirical experiments. Therefore, there is no mention of hardware specifications. |
| Software Dependencies | No | This is a theoretical paper presenting algorithms and hardness proofs, not empirical experiments. Therefore, there are no software dependencies for reproducibility in the context of running experiments. |
| Experiment Setup | No | This is a theoretical paper presenting algorithms and hardness proofs, not empirical experiments. Therefore, there is no experimental setup, hyperparameters, or training configurations to detail. |