The FWZ seminar is an old-style seminar originally taking place in Freiburg, Vienna or Zürich each four months (in average) full of blackboard talk discussions to present new proofs, concepts, ideas, papers, ... but without fixed schedule. So far we met at 30.6.2015 (Zürich), 28.-29.10.2015 (Freiburg), 16.-18.12.2015 (Vienna), 23.2.2016 (Freiburg), 18.-19.5.2016 (Vienna), 30.11.-2.12.2016 (Freiburg), 10.-11.4.2017 (Zürich), 18.-19.9.2017 (Vienna), 15.-16.3.2018 (Zürich), 1.-4.7.2018 (Xth edition in Strobl at Wolfgangsee, see the scheduled program), 15.-16.1.2019 (Vienna) to meander by discussions, presentations, ideas around the following focus areas below provided with some hopefully inspiring references (which have triggered or will trigger discussions).
We are happy to announce that our triangle became a rectangle in May 2019: the group from Padova joint, now we listen to the name FPZW. See the schedule of the event. We met again at 10.-11.10.2019 (Zürich).
Machine learning in mathematical Finance: ideas around machine-learning calibration functionals as presented in a seminal work by Andres Hernandez: Model Calibration with neural networks, SSRN.2812140 accompagnied by a talk on Bayesian Finance - a machine learning approach to a simple calibration problem, slides, 2017.
Why does machine learning work so well: some recent work of Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen: Optimal Approximation with Sparsely Connected Deep Neural Networks, arxiv.1705.01714.
Machine learning and non-linear PDEs: great recent work of Weinan E, Jiequn Han, Arnulf Jentzen: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, arxiv.1706.04702.
Machine learning and the right parameterization of a problem: recent work of Terry Lyons, Rough paths, Signatures and the modelling of functions on streams, arxiv.1405.4537.
Machine learning and unbiased estimation of risk: new ideas partly based on
Affine semimartingales: recent progress, see e.g. the slides.