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09/02/2023
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute pa...
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08/15/2023
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Objects we interact with and manipulate often share similar parts, such ...
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08/09/2023
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
This paper presents a novel Learning from Demonstration (LfD) method tha...
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08/01/2023
Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-fingered Hands
Efficient and accurate 3D object shape reconstruction contributes signif...
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07/20/2023
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport
Imitation learning (IL) seeks to teach agents specific tasks through exp...
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07/11/2023
Implicit regularisation in stochastic gradient descent: from single-objective to two-player games
Recent years have seen many insights on deep learning optimisation being...
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07/09/2023
Investigating the Edge of Stability Phenomenon in Reinforcement Learning
Recent progress has been made in understanding optimisation dynamics in ...
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04/11/2023
Actually Sparse Variational Gaussian Processes
Gaussian processes (GPs) are typically criticised for their unfavourable...
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03/30/2023
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
Offline reinforcement learning (RL) allows for the training of competent...
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03/29/2023
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory d...
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03/24/2023
Optimal Transport for Offline Imitation Learning
With the advent of large datasets, offline reinforcement learning (RL) i...
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02/01/2023
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
Short-term forecasting of solar photovoltaic energy (PV) production is i...
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09/15/2022
One-Shot Transfer of Affordance Regions? AffCorrs!
In this work, we tackle one-shot visual search of object parts. Given a ...
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07/04/2022
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation
In this paper, we propose to use a nonlinear adaptive PID controller to ...
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10/27/2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unkno...
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10/22/2021
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Many functions have approximately-known upper and/or lower bounds, poten...
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07/22/2021
Learning to Transfer: A Foliated Theory
Learning to transfer considers learning solutions to tasks in a such way...
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05/26/2021
Submodular Kernels for Efficient Rankings
Many algorithms for ranked data become computationally intractable as th...
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02/22/2021
Learning Contact Dynamics using Physically Structured Neural Networks
Learning physically structured representations of dynamical systems that...
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02/14/2021
Sliced Multi-Marginal Optimal Transport
We study multi-marginal optimal transport, a generalization of optimal t...
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02/14/2021
Healing Products of Gaussian Processes
Gaussian processes (GPs) are nonparametric Bayesian models that have bee...
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02/07/2021
Design of Dynamic Experiments for Black-Box Model Discrimination
Diverse domains of science and engineering require and use mechanistic m...
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01/06/2021
Cauchy-Schwarz Regularized Autoencoder
Recent work in unsupervised learning has focused on efficient inference ...
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11/14/2020
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
We propose an efficient algorithm to visualise symmetries in neural netw...
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11/08/2020
Pathwise Conditioning of Gaussian Processes
As Gaussian processes are integrated into increasingly complex problem s...
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10/29/2020
Matern Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functi...
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08/02/2020
A Foliated View of Transfer Learning
Transfer learning considers a learning process where a new task is solve...
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07/17/2020
Probabilistic Active Meta-Learning
Data-efficient learning algorithms are essential in many practical appli...
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07/14/2020
Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of ...
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06/26/2020
Stochastic Differential Equations with Variational Wishart Diffusions
We present a Bayesian non-parametric way of inferring stochastic differe...
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06/22/2020
Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing an...
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06/17/2020
Matern Gaussian processes on Riemannian manifolds
Gaussian processes are an effective model class for learning unknown fun...
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02/21/2020
Efficiently sampling functions from Gaussian process posteriors
Gaussian processes are the gold standard for many real-world modeling pr...
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10/21/2019
Variational Integrator Networks for Physically Meaningful Embeddings
Learning workable representations of dynamical systems is becoming an in...
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05/14/2019
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as w...
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05/13/2019
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
Differential privacy is concerned about the prediction quality while mea...
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10/05/2018
GPdoemd: a Python package for design of experiments for model discrimination
Model discrimination identifies a mathematical model that usefully expla...
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05/25/2018
Maximizing acquisition functions for Bayesian optimization
Bayesian optimization is a sample-efficient approach to global optimizat...
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03/20/2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Data efficiency, i.e., learning from small data sets, is critical in man...
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02/12/2018
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Healthcare companies must submit pharmaceutical drugs or medical devices...
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12/01/2017
The reparameterization trick for acquisition functions
Bayesian optimization is a sample-efficient approach to solving global o...
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08/19/2017
A Brief Survey of Deep Reinforcement Learning
Deep reinforcement learning is poised to revolutionise the field of AI a...
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06/20/2017
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Trial-and-error based reinforcement learning (RL) has seen rapid advance...
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05/30/2017
Identification of Gaussian Process State Space Models
The Gaussian process state space model (GPSSM) is a non-linear dynamical...
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05/29/2017
Neural Embeddings of Graphs in Hyperbolic Space
Neural embeddings have been used with great success in Natural Language ...
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03/07/2017
Customer Lifetime Value Prediction Using Embeddings
We describe the Customer LifeTime Value (CLTV) prediction system deploye...
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11/08/2016
Accelerating the BSM interpretation of LHC data with machine learning
The interpretation of Large Hadron Collider (LHC) data in the framework ...
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01/18/2016
Probabilistic Inference of Twitter Users' Age based on What They Follow
Twitter provides an open and rich source of data for studying human beha...
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01/15/2016
Real-Time Community Detection in Large Social Networks on a Laptop
For a broad range of research, governmental and commercial applications ...
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11/17/2015