Fitness functions map large combinatorial spaces of biological sequences to
properties of interest. Inferring these multimodal functions from experimental
data is a central task in modern protein engineering. Global epistasis models
are an effective and physically-grounded class of models for estimating fitness
functions from observed data. These models assume that a sparse latent function
is transformed by a monotonic nonlinearity to emit measurable fitness. Here we
demonstrate that minimizing contrastive loss functions, such as the
Bradley-Terry loss, is a simple and flexible technique for extracting the
sparse latent function implied by global epistasis. We argue by way of a
fitness-epistasis uncertainty principle that the nonlinearities in global
epistasis models can produce observed fitness functions that do not admit
sparse representations, and thus may be inefficient to learn from observations
when using a Mean Squared Error (MSE) loss (a common practice). We show that
contrastive losses are able to accurately estimate a ranking function from
limited data even in regimes where MSE is ineffective. We validate the
practical utility of this insight by showing contrastive loss functions result
in consistently improved performance on benchmark tasks