  An important class of techniques for resonant anomaly detection in high
energy physics builds models that can distinguish between reference and target
datasets, where only the latter has appreciable signal. Such techniques,
including Classification Without Labels (CWoLa) and Simulation Assisted
Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset.
They cannot take advantage of commonly-available multiple datasets and thus
cannot fully exploit available information. In this work, we propose
generalizations of CWoLa and SALAD for settings where multiple reference
datasets are available, building on weak supervision techniques. We demonstrate
improved performance in a number of settings with realistic and synthetic data.
As an added benefit, our generalizations enable us to provide finite-sample
guarantees, improving on existing asymptotic analyses.
