15 citations · 25 across the 4 of their papers we have counts for
4 papers
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
K S Sesh Kumar, Marc Peter Deisenroth
Differential privacy is concerned about the prediction quality while measuring the privacy impact on individuals whose information is contained in the data. We consider differentia…
High-dimensional Bayesian optimization using low-dimensional feature spaces
Riccardo Moriconi, Marc P. Deisenroth, K. S. Sesh Kumar
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of mach…
Convex Optimization for Parallel Energy Minimization
K. S. Sesh Kumar, Alvaro Barbero, Stefanie Jegelka +2
Energy minimization has been an intensely studied core problem in computer vision. With growing image sizes (2D and 3D), it is now highly desirable to run energy minimization algor…
Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs
K. S. Sesh Kumar, Francis Bach
We consider the problem of learning the structure of undirected graphical models with bounded treewidth, within the maximum likelihood framework. This is an NP-hard problem and mos…