We consider log-supermodular models on binary variables, which are probabilistic models
with negative log-densities which are submodular. These models provide probabilistic …

We consider the problem of approximate Bayesian inference in log-supermodular models.
These models encompass regular pairwise MRFs with binary variables, but allow to capture …

Submodular optimization has found many applications in machine learning and beyond. We
carry out the first systematic investigation of inference in probabilistic models defined …

Submodular and supermodular functions have found wide applicability in machine learning,
capturing notions such as diversity and regularity, respectively. These notions have deep …

Submodular functions are relevant to machine learning for at least two reasons:(1) some
problems may be expressed directly as the optimization of submodular functions and (2) the …

Submodular maximization problems appear in several areas of machine learning and data
science, as many useful modelling concepts such as diversity and coverage satisfy this …

Learning with non-modular losses is an important problem when sets of predictions are
made simultaneously. The main tools for constructing convex surrogate loss functions for set …

Stochastic optimization of continuous objectives is at the heart of modern machine learning.
However, many important problems are of discrete nature and often involve submodular …

We are motivated by large scale submodular optimization problems, where standard
algorithms, which treat the submodular functions in the value oracle model, do not scale. In …

Submodular functions have applications throughout machine learning, but in many settings,
we do not have direct access to the underlying function f. We focus on stochastic functions …