Program

Workshops

• Causal Inference meets Probabilistic Models (Thursday, September 17)
  
  Room: Zoom

Full Schedule

Day 1 | September 14 (Monday)

Summer School


8:30-9:00 Arrivals

9:00-10:30 An introduction to GPsRichard Wilkinson, The University of Nottingham [slides]

10:30-11:00 Coffee Break

11:00-12:30 Kernel designNicolas Durrande, PROWLER.io [slides]

12:30-13:45 Lunch

13:45-15:30 Lab session 1 and round tables with the speakers (Prof. Richard Wilkinson and Dr. Nicolas Durrande)

15:30-16:00 Coffee Break

16:00-17:00 Representation Learning with Gaussian ProcessesAndrew Gordon Wilson, Courant Institute of Mathematical Sciences and Center for Data Science, New York University [slides]


Day 2 | September 15 (Tuesday)

Summer School


8:30-9:00 Arrivals

9:00-10:30 Scalable Gaussian ProcessesZhenwen Dai, Spotify [slides]

10:30-11:00 Coffee Break

11:00-12:30 Unsupervised learning and Composite GPsCarl Henrik Ek, University of Cambridge [slides]

12:30-13:45 Lunch

13:45-15:30 Lab session 2 and round tables with the speakers (Dr. Zhenwen Dai and Dr. Carl Henrik Ek)

15:30-16:00 Coffee Break

16:00-17:00  Multi-task Gaussian process modelling for point process dataVirginia Aglietti, University of Warwick [slides]


Day 3 | September 16 (Wednesday)

Summer School


8:30-9:00 Arrivals

09:00-10:30 Deep GPsNeil Lawrence, University of Cambridge [slides]

10:30-11:00 Coffee Break

11:00-12:30 Introduction to Bayesian OptimisationJavier González, Microsoft Research Cambridge [slides]

12:30-13:45 Lunch

13:45-15:30 Lab session 3 and round tables with the speakers (Dr. Carl Henrik Ek and Dr. Javier González)

15:30-16:00 Coffee Break

16:00-17:00 Bayesian Neural Networks from a Gaussian Process PerspectiveAndrew Gordon Wilson, Courant Institute of Mathematical Sciences and Center for Data Science, New York University [slides]


Workshop September 17 (Thursday)

Causal Inference meets Probabilistic Models


8:30-9:00 Arrivals

9:00-16:30 Causal Inference meets Probabilistic Models