@inproceedings{641,
  abstract     = {We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches.},
  author       = {Trajkovska, Vera and Swoboda, Paul and Åström, Freddie and Petra, Stefanie},
  editor       = {Lauze, François and Dong, Yiqiu and Bjorholm Dahl, Anders},
  isbn         = {978-331958770-7},
  location     = {Kolding, Denmark},
  pages        = {323 -- 334},
  publisher    = {Springer},
  title        = {{Graphical model parameter learning by inverse linear programming}},
  doi          = {10.1007/978-3-319-58771-4_26},
  volume       = {10302},
  year         = {2017},
}

@inproceedings{646,
  abstract     = {We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations.},
  author       = {Kuske, Jan and Swoboda, Paul and Petra, Stefanie},
  editor       = {Lauze, François and Dong, Yiqiu and Bjorholm Dahl, Anders},
  isbn         = {978-331958770-7},
  location     = {Kolding, Denmark},
  pages        = {235 -- 246},
  publisher    = {Springer},
  title        = {{A novel convex relaxation for non binary discrete tomography}},
  doi          = {10.1007/978-3-319-58771-4_19},
  volume       = {10302},
  year         = {2017},
}