Informative planning seeks a sequence of actions that guide the robot to collect the most informative data to map a large environment or learn a dynamical system. Existing work in informative planning mainly focus on proposing new planners, and applying them to various robotic applications such as environmental monitoring, autonomous exploration, and system identification. The informative planners optimize an objective given by a probabilistic model, e.g. Gaussian process regression. In practice, the model can be easily affected by the ubiquitous sensing outliers, resulting in a misleading objective. A straightforward solution is to filter out the outliers in the sensing data stream using an off-the-shelf outlier detector. However, informative samples are also scarce by definition, so they might be falsely filtered out. In this paper, we propose a method to enable the robot to re-visit the locations where outliers were sampled besides optimizing the informative planning objective. By doing so, the robot can collect more samples in the vicinity of outliers and update the outlier detector to reduce the number of false alarms. This is achieved by designing a new objective on top of a Pareto variant of Monte Carlo tree search. We demonstrate that the proposed framework achieves better performance than simply applying an outlier detector.