Martin Jankowiak
Martin is a former particle physicist whose interest in data and modeling goes back to the Large Hadron Collider. After physics stops at Stanford and Heidelberg, he became a Research Scientist at the Center for Urban Science and Progress at NYU with a focus on applied machine learning research. Martin then joined a small machine learning start-up (Geometric Intelligence) with the happy end result that he joined AI Labs in March 2017.
Research Papers
Pyro: Deep Universal Probabilistic Programming
E. Bingham, J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, N. Goodman
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. [...] [PDF at arXiv]
Journal of Machine Learning Research (JMLR), 2018
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. [...] [PDF at arXiv]
Journal of Machine Learning Research (JMLR), 2018
Pathwise Derivatives for Multivariate Distributions
M. Jankowiak, T. Karaletsos
We exploit the link between the transport equation and derivatives of expectations to construct efficient pathwise gradient estimators for multivariate distributions. We focus on two main threads. [...] [PDF at arXiv]
International Conference on Artificial Intelligence and Statistics (AI STATS) (in submission), 2019
We exploit the link between the transport equation and derivatives of expectations to construct efficient pathwise gradient estimators for multivariate distributions. We focus on two main threads. [...] [PDF at arXiv]
International Conference on Artificial Intelligence and Statistics (AI STATS) (in submission), 2019
Pathwise Derivatives Beyond the Reparameterization Trick
M. Jankowiak, F. Obermeyer
We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We use this perspective to compute (approximate) pathwise gradients for probability distributions not directly amenable to the reparameterization trick: Gamma, Beta, and Dirichlet. [...] [PDF at arXiv]
International Conference on Machine Learning (ICML), 2018
We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We use this perspective to compute (approximate) pathwise gradients for probability distributions not directly amenable to the reparameterization trick: Gamma, Beta, and Dirichlet. [...] [PDF at arXiv]
International Conference on Machine Learning (ICML), 2018
