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

Contrastive losses as generalized models of global epistasis