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..
Uniform Welfare Guarantees Under Identical Subadditive Valuations
Authors: Siddharth Barman, Ranjani G. Sundaram
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish that, under identical subadditive valuations and in the demand oracle model, one can efficiently find a single allocation that approximates the optimal generalizedmean welfare to within a factor of 40 uniformly for all p ( , 1]. Hence, by way of a constantfactor approximation algorithm, we obtain novel results for maximizing Nash social welfare and egalitarian welfare for identical subadditive valuations. |
| Researcher Affiliation | Academia | Siddharth Barman1 and Ranjani G. Sundaram2 1Indian Institute of Science 2Chennai Mathematical Institute EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 ALG |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical algorithm and does not involve the use of datasets for training, validation, or testing. |
| Dataset Splits | No | The paper describes a theoretical algorithm and does not involve the use of datasets for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe software dependencies with version numbers. |
| Experiment Setup | No | This paper describes a theoretical algorithm and does not provide details on an experimental setup. |