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..
Fair and Efficient Online Allocations with Normalized Valuations
Authors: Vasilis Gkatzelis, Alexandros Psomas, Xizhi Tan5440-5447
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To prove our results, we leverage the fact that our algorithms have a closed-form expression for the agents allocations and utilities. We can use this fact and write a mathematical program that computes the worst-case approximation to the optimal welfare over all instances. We use variables vt for the value of agent 1 for item t and λt for the ratio between agents values. Even though this program is not itself convex (so at first glance it s unclear how useful it is), we show that under a suitable choice of variables and constraints, fixing some of the variables (i.e. treating them as constants) gives a linear program with respect to the remaining variables. |
| Researcher Affiliation | Academia | Vasilis Gkatzelis,1 Alexandros Psomas, 2 Xizhi Tan 1 1 Drexel University 2 Purdue University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical expressions and prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets or specify publicly available ones for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper mentions using 'numerical solvers' for proofs but does not specify any software names with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithm properties, not on specific experimental setup details like hyperparameters or training configurations. |