The Pareto Regret Frontier
The Pareto Regret Frontier
复制标题
帕累托遗憾边界
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Wouter M. Koolen
中科院分区：
文献类型：
--
作者：
Wouter M. Koolen
在线学习算法的性能保证通常采用后悔界限的形式，这表明与事后最好的专家相比，累积损失开销很小。在大型但结构化的专家集的常见情况下，我们通常希望与简单的专家相比保持特别小的遗憾，以与更复杂的专家相比适度的额外开销为代价。我们研究哪些遗憾可以实现，以及如何实现。
Performance guarantees for online learning algorithms typically take the form of regret bounds, which express that the cumulative loss overhead compared to the best expert in hindsight is small. In the common case of large but structured expert sets we typically wish to keep the regret especially small compared to simple experts, at the cost of modest additional overhead compared to more complex others. We study which such regret trade-offs can be achieved, and how.
We analyse regret w.r.t. each individual expert as a multi-objective criterion in the simple but fundamental case of absolute loss. We characterise the achievable and Pareto optimal trade-offs, and the corresponding optimal strategies for each sample size both exactly for each finite horizon and asymptotically.
影响因子：
5.2
作者：
van der Meer JW
通讯作者：
van der Meer JW