Sustainability and Machine Learning Group
Sustainability and Machine Learning Group
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Publications
Aligning Time Series on Incomparable Spaces (2021)
Learning Contact Dynamics using Physically Structured Neural Networks (2021)
Matérn Gaussian Processes on Graphs (2021)
Matérn Gaussian Processes on Riemannian Manifolds (2020)
Probabilistic Active Meta-Learning (2020)
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes (2020)
A Foliated View of Transfer Learning (2020)
Estimating Barycenters of Measures in High Dimensions (2020)
Efficiently Sampling Functions from Gaussian Process Posteriors (2020)
Healing Products of Gaussian Process Experts (2020)
Stochastic Differential Equations with Variational Wishart Diffusions (2020)
Aligning Time Series on Incomparable Spaces (2020)
Variational Integrator Networks for Physically Structured Embeddings (2020)
Mathematics for Machine Learning (2020)
High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes (2020)
Variational Integrator Networks (2019)
GPdoemd: A Python Package for Design of Experiments for Model Discrimination (2019)
Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018)
Probabilistic Model-based Imitation Learning (2013)
Seminar organization
Dan Roy: Admissibility is Bayes Optimality with Infinitesimals (Jul 2022)
Benjamin Chamberlain: A Continuous Perspective on Graph Neural Networks (Jul 2022)
Michalis Titsias: Functional Regularisation for Continual Learning with Gaussian Processes (Jun 2022)
Shahine Bouabid and Siu Chau: Deconditional Downscaling with Gaussian Processes (Feb 2022)
Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa (Oct 2021)
Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes (Mar 2021)
Ruha Benjamin: Reimagining the Default Settings of Technology & Society (Mar 2021)
César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations (Mar 2021)
Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (Feb 2021)
Dhruva Tirumala: Using behavior priors for data efficiency in Reinforcement Learning (Feb 2021)
Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry (Nov 2020)
Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks (Sep 2020)
Tim G. J. Rudner: Inter-domain Deep Gaussian Processes (Aug 2020)
Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions (Jul 2020)
Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus? (Jun 2020)
Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics (Mar 2020)
Laura Mansfield: Can we use machine learning to predict global patterns of climate change? (Mar 2020)
So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure (Mar 2020)
Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation (Feb 2020)
Keshi He: Toward AI-powered Robots Served for IndustrialProductions and Healthcare (Feb 2020)
Vincent Adam: Variational inference for Gaussian Process models: multi-GP regression and time-series models (Feb 2020)
Rituraj Kaushik: Data-efficient adaptation in robotics using priors from the simulator (Feb 2020)
Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization (Feb 2020)
Vincent Adam: Doubly Sparse Variational Gaussian Processes (Oct 2019)
Samuel Kaski: Probabilistic Modelling with Experts (Oct 2019)
Christian Walder: New Tricks for Estimating Gradients of Expectations (Sep 2019)
Emtiyaz Khan: Learning-Algorithms from Bayesian Principle (Sep 2019)
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