No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
Authors: Mengxiao Zhang, Ramiro Deo-Campo Vuong, Haipeng Luo
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 mengxiao-zhang@uiowa.edu Ramiro Deo-Campo Vuong Cornell University ramdcv@cs.cornell.edu Haipeng Luo University of Southern California haipengl@usc.edu |
| 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. |