Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, engineering task designing vaccines remains a challenge. In particular, genetic variability human makes it difficult design peptide that provide widespread immunity vaccinated populations. We introduce framework for evaluating uses probabilistic models, demonstr...

Submodularity (or concavity) is considered as an important property in the field of cooperative game theory. In this article, we characterize submodular minimum coloring games and submodular minimum vertex cover games. These characterizations immediately show that it can be decided in polynomial time that the minimum coloring game or the minimum vertex cover game on a given graph is submodular ...

The submodular knapsack set is the discrete lower level set of a submodular function. The modular case reduces to the classical linear 0-1 knapsack set. One motivation for studying the submodular knapsack polytope is to address 0-1 programming problems with uncertain coefficients. Under various assumptions, a probabilistic constraint on 0-1 variables can be modeled as a submodular knapsack set....

The submodular knapsack set is the discrete lower level set of a submodular function. The modular case reduces to the classical linear 0-1 knapsack set. One motivation for studying the submodular knapsack polytope is to address 0-1 programming problems with uncertain coefficients. Under various assumptions, a probabilistic constraint on 0-1 variables can be modeled as a submodular knapsack set....

In this paper we show the rst polynomial-time algorithm for the problem of minimizing submodular functions on the product of diamonds of nite size. This submodular function minimization problem is reduced to the membership problem for an associated polyhedron, which is equivalent to the optimization problem over the polyhedron, based on the ellipsoid method. The latter optimization problem is a...

In this paper, we consider the problem of constrained maximization of the minimum of a set of submodular functions, in which the goal is to find solutions that are robust to worst-case values of the objective functions. Unfortunately, this problem is both non-submodular and inapproximable. In the case where the submodular functions are monotone, an approximate solution can be found by relaxing ...

The conditional value at risk (CVaR) is a popular risk measure which enables risk-averse decision making under uncertainty. We consider maximizing the CVaR of a continuous submodular function, an extension of submodular set functions to a continuous domain. One example application is allocating a continuous amount of energy to each sensor in a network, with the goal of detecting intrusion or co...

Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization, and many more. Therefore, a lot of work has been concerned with maximizing or minimizing a submodular function, often subject to combinatorial constraints. Many of these algorithmic results exhib...