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Found 5 papers, 2 papers with code
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function.
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
This is the first work that analyzes the dual optimization problems of risk minimization problems in the context of differential privacy.
High-dimensional Bayesian optimization using low-dimensional feature spaces
Our approach allows for optimization of BO's acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem.
Convex Optimization for Parallel Energy Minimization
By exploiting results from convex and submodular theory, we reformulate the quadratic energy minimization problem as a total variation denoising problem, which, when viewed geometrically, enables the use of projection and reflection based convex methods.
Maximizing submodular functions using probabilistic graphical models
In a graphical model, the entropy of the joint distribution decomposes as a sum of marginal entropies of subsets of variables; moreover, for any distribution, the entropy of the closest distribution factorizing in the graphical model provides an bound on the entropy.
