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06/09/2023
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature
Real-world optimisation problems often feature complex combinations of (...
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06/08/2023
Bayesian Optimisation of Functions on Graphs
The increasing availability of graph-structured data motivates the task ...
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12/28/2022
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has pro...
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10/28/2022
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature
A wide variety of battery models are available, and it is not always obv...
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10/18/2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Optimizing expensive-to-evaluate black-box functions of discrete (and po...
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10/04/2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Gaussian processes (GPs) produce good probabilistic models of functions,...
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09/01/2022
Bézier Gaussian Processes for Tall and Wide Data
Modern approximations to Gaussian processes are suitable for "tall data"...
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07/19/2022
Bayesian Generational Population-Based Training
Reinforcement learning (RL) offers the potential for training generally ...
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06/09/2022
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Offline reinforcement learning has shown great promise in leveraging lar...
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06/09/2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Calculation of Bayesian posteriors and model evidences typically require...
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02/15/2022
Robust Multi-Objective Bayesian Optimization Under Input Noise
Bayesian optimization (BO) is a sample-efficient approach for tuning des...
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11/04/2021
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide ran...
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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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10/08/2021
Revisiting Design Choices in Model-Based Offline Reinforcement Learning
Offline reinforcement learning enables agents to leverage large pre-coll...
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07/05/2021
Universal Approximation of Functions on Sets
Modelling functions of sets, or equivalently, permutation-invariant func...
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06/14/2021
Marginalising over Stationary Kernels with Bayesian Quadrature
Marginalising over families of Gaussian Process kernels produces flexibl...
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02/14/2021
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notorio...
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10/29/2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic Variational Objective (T...
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06/13/2020
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Neural architecture search (NAS) automates the design of deep neural net...
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09/20/2019
Bayesian Optimization for Iterative Learning
The success of deep (reinforcement) learning systems crucially depends o...
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08/22/2019
Adaptive Configuration Oracle for Online Portfolio Selection Methods
Financial markets are complex environments that produce enormous amounts...
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06/20/2019
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuo...
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05/07/2019
Knowing The What But Not The Where in Bayesian Optimization
Bayesian optimization has demonstrated impressive success in finding the...
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02/26/2019
Automated Model Selection with Bayesian Quadrature
We present a novel technique for tailoring Bayesian quadrature (BQ) to m...
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02/22/2019
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Stochastic differential equations are an important modeling class in man...
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02/17/2019
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Parameter inference in ordinary differential equations is an important p...
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01/29/2019
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
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11/26/2018
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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07/17/2018
Battery health prediction under generalized conditions using a Gaussian process transition model
Accurately predicting the future health of batteries is necessary to ens...
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05/27/2018
Contextual Policy Optimisation
Policy gradient methods have been successfully applied to a variety of r...
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05/22/2018
Optimization, fast and slow: optimally switching between local and Bayesian optimization
We develop the first Bayesian Optimization algorithm, BLOSSOM, which sel...
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03/28/2018
Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine lear...
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03/09/2018
Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dyn...
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12/07/2017
Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries
Accurate on-board capacity estimation is of critical importance in lithi...
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07/13/2017
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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07/13/2017
Distributionally Ambiguous Optimization Techniques in Batch Bayesian Optimization
We propose a novel, theoretically-grounded, acquisition function for bat...
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05/02/2017
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
In this paper we use Gaussian Process (GP) regression to propose a novel...
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03/23/2017
Distribution of Gaussian Process Arc Lengths
We present the first treatment of the arc length of the Gaussian Process...
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03/16/2017
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of b...
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03/13/2017
Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for black-box function...
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05/24/2016
Alternating Optimisation and Quadrature for Robust Control
Bayesian optimisation has been successfully applied to a variety of rein...
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02/22/2016
Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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12/03/2015
Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
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10/27/2015
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes
We present Blitzkriging, a new approach to fast inference for Gaussian p...
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09/08/2015
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression
Online Passive-Aggressive (PA) learning is a class of online margin-base...
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06/08/2015
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference pro...
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06/03/2015
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithm...
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11/03/2014
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic mod...
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11/02/2014
Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for con...
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07/30/2014