论文标题
在风险标准下，接近最佳的MNL土匪
Near-Optimal MNL Bandits Under Risk Criteria
论文作者
论文摘要
我们研究MNL土匪，这是风险标准下传统多军匪徒问题的变体。与普通的预期收入不同，风险标准是在行业和业务中广泛使用的更一般目标。我们为广泛的风险标准设计算法，包括但不限于众所周知的有条件价值，夏普比率和熵风险，并证明它们遭受了近乎最理想的遗憾。作为补充，我们还使用合成数据和真实数据进行实验，以显示我们提出的算法的经验性能。
We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general goals widely used in industries and bussiness. We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio and entropy risk, and prove that they suffer a near-optimal regret. As a complement, we also conduct experiments with both synthetic and real data to show the empirical performance of our proposed algorithms.
