Partial monitoring is a rich framework for sequential decision making under
uncertainty that generalizes many well known bandit models, including linear,
combinatorial and dueling bandits. We introduce information directed sampling
(IDS) for stochastic partial monitoring with a linear reward and observation
structure. IDS achieves adaptive worst-case regret rates that depend on precise
observability conditions of the game. Moreover, we prove lower bounds that
classify the minimax regret of all finite games into four possible regimes. IDS
achieves the optimal rate in all cases up to logarithmic factors, without
tuning any hyper-parameters. We further extend our results to the contextual
and the kernelized setting, which significantly increases the range of possible
applications.

该论文提出信息导向抽样方法在带线性奖励结构的随机局部监控中实现了自适应的最坏情况遗憾率，并且针对所有有限博弈分类了最小最大遗憾，IDS 在所有情况下都实现了最优率。

线性部分监督的信息引导采样

Information Directed Sampling for Linear Partial Monitoring

We present a certifiably globally optimal algorithm for determining the
extrinsic calibration between two sensors that are capable of producing
independent egomotion estimates. This problem has been previously solved using
a variety of techniques, including local optimization approaches that have no
formal global optimality guarantees. We use a quadratic objective function to
formulate calibration as a quadratically constrained quadratic program (QCQP).
By leveraging recent advances in the optimization of QCQPs, we are able to use
existing semidefinite program (SDP) solvers to obtain a certifiably global
optimum via the Lagrangian dual problem. Our problem formulation can be
globally optimized by existing general-purpose solvers in less than a second,
regardless of the number of measurements available and the noise level. This
enables a variety of robotic platforms to rapidly and robustly compute and
certify a globally optimal set of calibration parameters without a prior
estimate or operator intervention. We compare the performance of our approach
with a local solver on extensive simulations and multiple real datasets.
Finally, we present necessary observability conditions that connect our
approach to recent theoretical results and analytically support the empirical
performance of our system.

使用 QCQP 和 SDP 求解算法，可以快速高效地获得两个独立传感器之间的外部校准参数，且具有全局最优性，同时本文还提出了必要的可观测性条件，来验证算法的实际性能。