研究人员开发了一种新的基于投影的算法，用于约束在线凸优化（COCO），该算法显著提高了性能。该算法实现了强凸损失的对数遗憾和累积约束违反（CCV），CCV方面实现了指数级改进。对于一般凸损失，它保持了最优遗憾，同时降低了CCV。 AI
影响 在与机器学习相关的优化算法方面引入了理论改进。
排序理由 该集群包含一篇详细介绍新算法及其理论保证的学术论文。
在 Hugging Face Daily Papers 阅读 →
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We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve small static regret against the best point satisf…
arXiv:2605.21107v1 Announce Type: cross Abstract: We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve…
We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve small static regret against the best point satisf…