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 [1].
Optimal Anonymous Independent Reward Scheme Design
Authors: Mengjing Chen, Pingzhong Tang, Zihe Wang, Shenke Xiao, Xiwang Yang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an ef๏ฌcient algorithm to solve it. |
| Researcher Affiliation | Collaboration | Mengjing Chen1 , Pingzhong Tang1 , Zihe Wang2,3 , Shenke Xiao1 and Xiwang Yang4 1Tsinghua University 2Renmin University of China 3Beijing Key Laboratory of Big Data Management and Analysis Methods 4Bytedance EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 An O(m) algorithm to compute SB and Algorithm 2 Algorithm to solve P2 |
| Open Source Code | No | The paper does not mention releasing any source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |