Sustainability and Machine Learning Group
Sustainability and Machine Learning Group
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Blog posts
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels (Dec 2021)
Matérn Gaussian Processes on Graphs (Apr 2021)
Learning Contact Dynamics using Physically Structured Neural Networks (Aug 2020)
Variational Integrator Networks (Aug 2020)
Aligning Time Series on Incomparable Space (Jul 2020)
Efficiently Sampling Functions from Gaussian Process Posteriors (Jul 2020)
Matérn Gaussian Processes on Riemannian Manifolds (Jun 2020)
Publications
A Unifying Variational Framework for Gaussian Process Motion Planning (2024)
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels (2021)
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels (2021)
Pathwise Conditioning of Gaussian Processes (2021)
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)
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models (2020)
Efficiently Sampling Functions from Gaussian Process Posteriors (2020)
Aligning Time Series on Incomparable Spaces (2020)
Variational Integrator Networks for Physically Structured Embeddings (2020)
Asynchronous Gibbs Sampling (2020)
Variational Integrator Networks for Physically Meaningful Embeddings (2019)
Seminar organization
Willie Neiswanger: Going Beyond Global Optima with Bayesian Algorithm Execution (Jul 2021)
Thu Nguyen Phuoc: Neural rendering and inverse rendering using physical inductive biases (Oct 2020)
Michael Lutter: Inductive Biases for Learning Robot Control (Feb 2020)
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