Probabilistic Numerical Methods for Machine Learning: recent trends


The conference will take place in the lecture hall Alan Turing L001, building L, at Faculté des Sciences (adress : 2 Boulevard Lavoisier 49045 Angers) see a map here.

Information about  the conference can also be found here.


-Keywords : Particle methods, Neural Networks, GAN, Wasserstein distance, Stochastic Optimization and MCMC methods, Robustness and sensitivity.

-Abstract : Probabilistic numerical methods are at the heart of machine learning algorithms. They play an important role in related optimization problems and also certainly for sampling methods which can be used for calibration, generation of new datas or sensitivity problems (among other applications). The increasing complexity of machine learning algorithms involves many new theoretical and practical problems. The workshop thus  aims at focusing on new challenges in probabilistic numerical methods for machine learning, especially in particle methods, stochastic optimization and MCMC, robustness and sensitivity, and Wasserstein computation problems.