4.4k citations · 6.2k across the 79 of their papers we have counts for
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Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy +3
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-mak…
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne
Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functi…
Learning to Transfer: A Foliated Theory
Janith Petangoda, Marc Peter Deisenroth, Nicholas A. M. Monk
Learning to transfer considers learning solutions to tasks in a such way that relevant knowledge can be transferred from known task solutions to new, related tasks. This is importa…
The Graph Cut Kernel for Ranked Data
Michelangelo Conserva, Marc Peter Deisenroth, K S Sesh Kumar
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challeng…
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…
Healing Products of Gaussian Processes
Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala +1
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems. One of the approaches to alleviate their cubic training…
Cauchy-Schwarz Regularized Autoencoder
Linh Tran, Maja Pantic, Marc Peter Deisenroth
Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models by maximizing the evidence (marginal likeliho…
Learning Contact Dynamics using Physically Structured Neural Networks
Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson +1
Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics…
Sliced Multi-Marginal Optimal Transport
Samuel Cohen, Alexander Terenin, Yannik Pitcan +3
Multi-marginal optimal transport enables one to compare multiple probability measures, which increasingly finds application in multi-task learning problems. One practical limitatio…
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi +3
Diverse domains of science and engineering use parameterised mechanistic models. Engineers and scientists can often hypothesise several rival models to explain a specific process o…
Copula Flows for Synthetic Data Generation
Sanket Kamthe, Samuel Assefa, Marc Deisenroth
The ability to generate high-fidelity synthetic data is crucial when available (real) data is limited or where privacy and data protection standards allow only for limited use of t…