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..
Envy-Free House Allocation under Uncertain Preferences
Authors: Haris Aziz, Isaiah Iliffe, Bo Li, Angus Ritossa, Ankang Sun, Mashbat Suzuki
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the envy-free house allocation problem when agents have uncertain preferences over items and consider several well-studied preference uncertainty models. The central problem that we focus on is computing an allocation that has the highest probability of being envy-free. We show that each model leads to a distinct set of algorithmic and complexity results, including detailed results on (in-)approximability. En route, we consider two related problems of checking whether there exists an allocation that is possibly or necessarily envy-free. We give a complete picture of the computational complexity of these two problems for all the uncertainty models we consider. |
| Researcher Affiliation | Academia | Haris Aziz1, Isaiah Iliffe1, Bo Li2, Angus Ritossa1, Ankang Sun2, Mashbat Suzuki1 1UNSW Sydney 2Hong Kong Polytechnic University EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Additive Approximation for MAX-PROBEF |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository or explicitly state that the code for the described methodology is being released. |
| Open Datasets | No | This paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not report on specific hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This paper is theoretical and does not include details on an experimental setup, hyperparameters, or training configurations. |