3 research outputs found
Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and
experimental design. However, it is computationally intractable for several
important classes of statistical models, including energy-based models and
simulator-based models. Contrastive learning is an intuitive and
computationally feasible alternative to likelihood-based learning. We here
first provide an introduction to contrastive learning and then show how we can
use it to derive methods for diverse statistical problems, namely parameter
estimation for energy-based models, Bayesian inference for simulator-based
models, as well as experimental design.Comment: Accepted to Behaviormetrik