• Scalability of Gaussian Processes (2021)
    GPSS 2021, online
    [slides]

  • Variational Gaussian Processes (2020)
    GPSS 2020, online
    [slides]

  • Scaling up Gaussian processes for real-world data (2020)
    Gaussian Processes Cambridge Meetup, Cambridge, UK
    [slides]

  • Bayesian Optimization: Basics & Challenges (2020)
    Boston Machine Learning Meetup, Boston, MA, US
    [slides]

  • What uncertainty do we get? (2019)
    Workshop on Uncertainty Propagation in Composite Models, Munich, Germany
    [slides]

  • Scalable Gaussian Processes (2019)
    GPSS 2019, Sheffield, UK
    [slides]

  • Scalable Gaussian Processes (2018)
    GPSS 2018, Sheffield, UK
    [slides]

  • Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes (2017)
    Gaussian Process Approximation Workshop 2017, Berlin, Germany
    [slides]

  • Preferential Bayesian Optmization (2017)
    ICML 2017, Sydney, Australia
    [video] [slides]

  • Scaling Up Deep Gaussian Processes (2016)
    Deep Probabilistic Model Meeting, London, UK.
    [slides]

  • Variational Auto-Encoded Deep Gaussian Processes (2015)
    Alan Turing Institute Deep Learning Scoping Workshop, Edinburgh, UK.

  • Probabilistic Unsupervised Learning with Latent Variable Models (2015)
    Cluster of Excellence Hearing4all and Dept for Medical Physics and Acoustics, Carl von Ossietzky University Oldenburg, Germany.

  • Unsupervised Learning with Latent Variable Models (2015)
    Computational and Biological Learning Lab, University of Cambridge, UK.

  • Variational Hierarchical Communities of Experts (2015)
    CSML lunch seminar, UCL, UK.
    [slides]

  • Spike and Slab GPLVM for Extracting Regulator Activity Profiles (2015)
    RADIANT meeting, Zurich, Switzerland.

  • Spike and Slab GPLVM for Gene Express Analysis (2014)
    Faculty of Life Sciences, University of Manchester, UK.

  • Speeding Up Bayesian GP-LVM with Parallelization and GPU accelerations (2014)
    RADIANT meeting, Heidelberg, Germany

  • What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach (2013)
    Redwood Center for Theoretical Neuroscience, UC Berkeley, US

  • Unsupervised Learning of Invariant Object Representations – A Probabilistic Generative Modeling Approach (2013)
    LEAR, Grenoble, France

  • Autonomous Cleaning of Corrupted Scanned Documents – A Generative Modeling Approach (2012)
    oral presentation, Computer Vision and Pattern Recognition (CVPR) 2012, Providence, Rhode Island, US