181 citations · 620 across the 171 of their papers we have counts for
6 papers · 1 filter
An Online-Learning Approach to Inverse Optimization
Andreas Bärmann, Alexander Martin, Sebastian Pokutta +1
In this paper, we demonstrate how to learn the objective function of a decision-maker while only observing the problem input data and the decision-maker's corresponding decisions o…
Principled Deep Neural Network Training through Linear Programming
Daniel Bienstock, Gonzalo Muñoz, Sebastian Pokutta
Deep learning has received much attention lately due to the impressive empirical performance achieved by training algorithms. Consequently, a need for a better theoretical understa…
Restarting Frank-Wolfe: Faster Rates Under Hölderian Error Bounds
Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta
Conditional Gradient algorithms (aka Frank-Wolfe algorithms) form a classical set of methods for constrained smooth convex minimization due to their simplicity, the absence of proj…
Efficient algorithms for robust submodular maximization under matroid constraints
Sebastian Pokutta, Mohit Singh, Alfredo Torrico
In this work, we consider robust submodular maximization with matroid constraints. We give an efficient bi-criteria approximation algorithm that outputs a small family of feasible…
New Limits of Treewidth-based tractability in Optimization
Yuri Faenza, Gonzalo Muñoz, Sebastian Pokutta
Sparse structures are frequently sought when pursuing tractability in optimization problems. They are exploited from both theoretical and computational perspectives to handle compl…
Blended Conditional Gradients: the unconditioning of conditional gradients
Gábor Braun, Sebastian Pokutta, Dan Tu +1
We present a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank--Wolfe algorithm (also called conditional gradient…