The modelling of continuoustime dynamical systems from uncertain observations is an important task that comes up in a wide range of applications ranging from numerical weather prediction over finance to genetic networks and motion capture in video. Often, we may assume that the dynamical models are formulated by systems of differential equations. In a Bayesian approach, we may then incorporate a priori knowledge about the dynamics by providing probability distributions over the unknown functions, which correspond for example to driving forces and appear as coefficients or parameters in the differential equations. Hence, such functions become stochastic processes in a probabilistic Bayesian framework. Gaussian processes (GPs) provide a natural and flexible framework in such circumstances. The use of GPs in the learning of functions from data is now a wellestablished technique in Machine Learning. Nevertheless, their application to dynamical systems becomes highly nontrivial when the dynamics is nonlinear in the (Gaussian) parameter functions as closed form analytical posterior predictions (even in the case of Gaussian observation noise) are no longer possible. Moreover, their computation requires the entire underlying Gaussian latent process at all times (not just at the discrete observation times). Hence, inference of the dynamics would require nontrivial sampling methods or approximation techniques. The aim of this workshop is to provide a forum for discussing open problems related to stochastic dynamical systems, their links to Bayesian inference and their relevance to Machine Learning.
Author Information
Manfred Opper (Technische Universitaet Berlin)
Cedric Archambeau (Amazon, Berlin)
John ShaweTaylor (UCL)
John ShaweTaylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.
More from the Same Authors

2018 Poster: PACBayes bounds for stable algorithms with instancedependent priors »
Omar Rivasplata · Emilio ParradoHernandez · John ShaweTaylor · Shiliang Sun · Csaba Szepesvari 
2018 Poster: Empirical Risk Minimization Under Fairness Constraints »
Michele Donini · Luca Oneto · Shai BenDavid · John ShaweTaylor · Massimiliano Pontil 
2018 Tutorial: Statistical Learning Theory: a Hitchhiker's Guide »
John ShaweTaylor · Omar Rivasplata 
2017 Workshop: Workshop on Prioritising Online Content »
John ShaweTaylor · Massimiliano Pontil · Nicolò CesaBianchi · Emine Yilmaz · Chris Watkins · Sebastian Riedel · Marko Grobelnik 
2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
Ricardo Silva · Panagiotis Toulis · John ShaweTaylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan 
2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
Ricardo Silva · John ShaweTaylor · Adith Swaminathan · Thorsten Joachims 
2015 Poster: A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding »
Yuval Harel · Ron Meir · Manfred Opper 
2015 Spotlight: A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding »
Yuval Harel · Ron Meir · Manfred Opper 
2014 Workshop: Learning Semantics »
Cedric Archambeau · Antoine Bordes · Leon Bottou · Chris J Burges · David Grangier 
2014 Poster: Multilabel Structured Output Learning with Random Spanning Trees of MaxMargin Markov Networks »
Mario Marchand · Hongyu Su · Emilie Morvant · Juho Rousu · John ShaweTaylor 
2012 Workshop: MultiTradeoffs in Machine Learning »
Yevgeny Seldin · Guy Lever · John ShaweTaylor · Nicolò CesaBianchi · Yacov Crammer · Francois Laviolette · Gabor Lugosi · Peter Bartlett 
2011 Workshop: Choice Models and Preference Learning »
JeanMarc Andreoli · Cedric Archambeau · Guillaume Bouchard · Shengbo Guo · Kristian Kersting · Scott Sanner · Martin Szummer · Paolo Viappiani · Onno Zoeter 
2011 Workshop: New Frontiers in Model Order Selection »
Yevgeny Seldin · Yacov Crammer · Nicolò CesaBianchi · Francois Laviolette · John ShaweTaylor 
2011 Session: Spotlight Session 7 »
Cedric Archambeau 
2011 Session: Oral Session 9 »
Cedric Archambeau 
2011 Poster: Inference in continuous time changepoint point models »
Florian Stimberg · Manfred Opper · Guido Sanguinetti · Andreas Ruttor 
2011 Poster: PACBayesian Analysis of Contextual Bandits »
Yevgeny Seldin · Peter Auer · Francois Laviolette · John ShaweTaylor · Ronald Ortner 
2011 Poster: Sparse Bayesian MultiTask Learning »
Cedric Archambeau · Shengbo Guo · Onno Zoeter 
2010 Poster: Approximate inference in continuous time GaussianJump processes »
Manfred Opper · Andreas Ruttor · Guido Sanguinetti 
2010 Talk: Opening Remarks and Awards »
Richard Zemel · Terrence J Sejnowski · John ShaweTaylor 
2009 Workshop: Grammar Induction, Representation of Language and Language Learning »
Alex Clark · Dorota Glowacka · John ShaweTaylor · Yee Whye Teh · Chris J Watkins 
2008 Workshop: Learning from Multiple Sources »
David R Hardoon · Gayle Leen · Samuel Kaski · John ShaweTaylor 
2008 Workshop: New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces »
MariaFlorina F Balcan · Shai BenDavid · Avrim Blum · Kristiaan Pelckmans · John ShaweTaylor 
2008 Poster: Sparse probabilistic projections »
Cedric Archambeau · Francis Bach 
2008 Spotlight: Sparse probabilistic projections »
Cedric Archambeau · Francis Bach 
2008 Poster: Improving on Expectation Propagation »
Manfred Opper · Ulrich Paquet · Ole Winther 
2008 Spotlight: Improving on Expectation Propagation »
Manfred Opper · Ulrich Paquet · Ole Winther 
2008 Poster: Theory of matching pursuit »
Zakria Hussain · John ShaweTaylor 
2007 Workshop: Music, Brain and Cognition. Part 1: Learning the Structure of Music and Its Effects On the Brain »
David R Hardoon · Eduardo ReckMiranda · John ShaweTaylor 
2007 Poster: Variational inference for Markov jump processes »
Manfred Opper · Guido Sanguinetti 
2007 Poster: Variational Inference for Diffusion Processes »
Cedric Archambeau · Manfred Opper · Yuan Shen · Dan Cornford · John ShaweTaylor 
2006 Poster: Tighter PACBayes Bounds »
Amiran Ambroladze · Emilio ParradoHernandez · John ShaweTaylor