  We present the first evidence that adaptive learning techniques can boost the
discovery of unusual objects within astronomical light curve data sets. Our
method follows an active learning strategy where the learning algorithm chooses
objects which can potentially improve the learner if additional information
about them is provided. This new information is subsequently used to update the
machine learning model, allowing its accuracy to evolve with each new
information. For the case of anomaly detection, the algorithm aims to maximize
the number of scientifically interesting anomalies presented to the expert by
slightly modifying the weights of a traditional Isolation Forest (IF) at each
iteration. In order to demonstrate the potential of such techniques, we apply
the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light
curves from the PLAsTiCC challenge and real light curves from the Open
Supernova Catalog. We compare the AAD results to those of a static IF. For both
methods, we performed a detailed analysis for all objects with the ~2% highest
anomaly scores. We show that, in the real data scenario, AAD was able to
identify ~80\% more true anomalies than the IF. This result is the first
evidence that AAD algorithms can play a central role in the search for new
physics in the era of large scale sky surveys.
