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..
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
Authors: Mengxiao Zhang, Ramiro Deo-Campo Vuong, Haipeng Luo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we provide a complete answer to this question in various settings. Specifically, in stochastic N-agent K-armed bandits, we develop an algorithm with e O(K 2 N T N 1 N ) regret and prove that the dependence on T is tight... we prove that no algorithm can achieve sublinear regret. To circumvent such negative results, we further consider a setting with full-information feedback and design two algorithms with T-regret |
| Researcher Affiliation | Academia | Mengxiao Zhang University of Iowa EMAIL Ramiro Deo-Campo Vuong Cornell University EMAIL Haipeng Luo University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1 UCB for N-agent K-armed NSW maximization... Algorithm 2 FTRL for N-agent K-armed SWF maximization with full-info feedback |
| Open Source Code | No | No explicit statement about releasing code or links to a repository was found. The NeurIPS checklist mentions: 'This paper does not release new assets.' |
| Open Datasets | No | This paper does not include experiments. Therefore, no dataset information for training, validation, or testing is provided. |
| Dataset Splits | No | This paper does not include experiments. Therefore, no dataset information for training, validation, or testing is provided. |
| Hardware Specification | No | This paper does not include experiments, and therefore no hardware specifications are provided. |
| Software Dependencies | No | No specific ancillary software details with version numbers were mentioned in the paper. |
| Experiment Setup | No | This paper does not include experiments or specific experimental setup details like hyperparameters or training configurations. |