Student Projects

• Dynamical Compuation Graphs in Universal Probabilistic Programming

PhD Thesis

• Sum-Product Networks for Complex Modelling Scenarios

Publications

• Conference paper Trapp M., Peharz R., Pernkopf F. & Rassmusen C. (2020) Deep Structured Mixtures of Gaussian Processes. in 23rd International Conference on Artificial Intelligence and Statistics (pp. 2251-2261). [more info]
• Conference paper Peharz R., Lang S., Vergari A., Stelzner K., Molina A., Trapp M., Broeck G., Kersting K. & Ghahramani Z. (2020) Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.. in 37th International Conference on Machine Learning (pp. 7563-7574). [more info]
• Conference paper Gabler P., Trapp M., Ge H. & Pernkopf F. (2019) Graph Tracking in Dynamic Probabilistic Programs via Source Transformations.. [more info]
• Conference paper Trapp M., Peharz R., Ge H., Pernkopf F. & Ghaharamani Z. (2019) Bayesian Learning of Sum-Product Networks.. [more info]
• Conference paper Trapp M., Peharz R. & Pernkopf F. (2019) Optimisation of Overparametrized Sum-Product Networks. in Workshop on Tractable Probabilistic Modeling at ICML. [more info]
• Conference paper Peharz R., Vergari A., Stelzner K., Molina A., Shao X., Trapp M., Kersting K. & Ghahramani Z. (2019) Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning.. [more info]
• Conference paper Trapp M., Peharz R., Rassmusen C. & Pernkopf F. (2018) Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks.. [more info]
• Conference paper Trapp M., Madl T., Peharz R., Pernkopf F. & Trappl R. (2017) Safe Semi-Supervised Learning of Sum-Product Networks.. [more info]
• Conference paper Trapp M., Peharz R., Skowron M., Madl T., Pernkopf F. & Trappl R. (2016) Structure Inference in Sum-Product Networks using Infinite Sum-Product Trees.. [more info]