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

    Room: Zoom
  • Day 1 | Monday, September 14 Summer School
    8:30- 9:00 Arrivals

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

    10:30- 11:00 Coffee Break

    11:00- 12:30 Kernel design Nicolas 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 Processes Andrew Gordon Wilson Courant Institute of Mathematical Sciences and Center for Data Science, New York University [slides]
  • Day 2 | Tuesday, September 15 Summer School
    8:30- 9:00 Arrivals

    9:00- 10:30 Scalable Gaussian Processes Zhenwen Dai Spotify [slides]

    10:30- 11:00 Coffee Break

    11:00- 12:30 Unsupervised learning and Composite GPs Carl 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 data Virginia Aglietti University of Warwick [slides]
  • Day 3 | Wednesday, September 16 Summer School
    8:30- 9:00 Arrivals

    09:00- 10:30 Deep GPs Neil Lawrence University of Cambridge [slides]

    10:30- 11:00 Coffee Break

    11:00- 12:30 Introduction to Bayesian Optimisation Javier 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 Perspective Andrew Gordon Wilson Courant Institute of Mathematical Sciences and Center for Data Science, New York University [slides]
  • Workshop Thursday, September 17 Causal Inference meets Probabilistic Models
    8:30- 9:00 Arrivals

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