Michael A. Osborne

research

∙ 12/28/2022

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

KL-regularized reinforcement learning from expert demonstrations has pro...

0 Tim G. J. Rudner, et al. ∙

research

∙ 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...

7 Masaki Adachi, et al. ∙

research

∙ 10/18/2022

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Optimizing expensive-to-evaluate black-box functions of discrete (and po...

16 Samuel (Sam) Daulton, et al. ∙

research

∙ 10/04/2022

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels

Gaussian processes (GPs) produce good probabilistic models of functions,...

0 Michael K. Cohen, et al. ∙

research

∙ 09/01/2022

Bézier Gaussian Processes for Tall and Wide Data

Modern approximations to Gaussian processes are suitable for "tall data"...

0 Martin Jørgensen, et al. ∙

research

∙ 07/19/2022

Bayesian Generational Population-Based Training

Reinforcement learning (RL) offers the potential for training generally ...

11 Xingchen Wan, et al. ∙

research

∙ 06/09/2022

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Offline reinforcement learning has shown great promise in leveraging lar...

0 Cong Lu, et al. ∙

research

∙ 06/09/2022

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Calculation of Bayesian posteriors and model evidences typically require...

9 Masaki Adachi, et al. ∙

research

∙ 02/15/2022

Robust Multi-Objective Bayesian Optimization Under Input Noise

Bayesian optimization (BO) is a sample-efficient approach for tuning des...

0 Samuel (Sam) Daulton, et al. ∙

research

∙ 11/04/2021

Adversarial Attacks on Graph Classification via Bayesian Optimisation

Graph neural networks, a popular class of models effective in a wide ran...

14 Xingchen Wan, et al. ∙

research

∙ 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...

13 Vu Nguyen, et al. ∙

research

∙ 10/08/2021

Revisiting Design Choices in Model-Based Offline Reinforcement Learning

Offline reinforcement learning enables agents to leverage large pre-coll...

3 Cong Lu, et al. ∙

research

∙ 07/05/2021

Universal Approximation of Functions on Sets

Modelling functions of sets, or equivalently, permutation-invariant func...

4 Edward Wagstaff, et al. ∙

research

∙ 06/14/2021

Marginalising over Stationary Kernels with Bayesian Quadrature

Marginalising over families of Gaussian Process kernels produces flexibl...

0 Saad Hamid, et al. ∙

research

∙ 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...

6 Xingchen Wan, et al. ∙

research

∙ 10/29/2020

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

Achieving the full promise of the Thermodynamic Variational Objective (T...

11 Vu Nguyen, et al. ∙

research

∙ 06/13/2020

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search

Neural architecture search (NAS) automates the design of deep neural net...

0 Vu Nguyen, et al. ∙

research

∙ 09/20/2019

Bayesian Optimization for Iterative Learning

The success of deep (reinforcement) learning systems crucially depends o...

10 Vu Nguyen, et al. ∙

research

∙ 08/22/2019

Adaptive Configuration Oracle for Online Portfolio Selection Methods

Financial markets are complex environments that produce enormous amounts...

9 Favour M. Nyikosa, et al. ∙

research

∙ 06/20/2019

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

Efficient optimisation of black-box problems that comprise both continuo...

5 Binxin Ru, et al. ∙

research

∙ 05/07/2019

Knowing The What But Not The Where in Bayesian Optimization

Bayesian optimization has demonstrated impressive success in finding the...

25 Vu Nguyen, et al. ∙

research

∙ 02/26/2019

Automated Model Selection with Bayesian Quadrature

We present a novel technique for tailoring Bayesian quadrature (BQ) to m...

16 Henry Chai, et al. ∙

research

∙ 02/22/2019

AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in man...

12 Gabriele Abbati, et al. ∙

research

∙ 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...

10 Philippe Wenk, et al. ∙

research

∙ 01/29/2019

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Batch Bayesian optimisation (BO) has been successfully applied to hyperp...

14 Ahsan S. Alvi, et al. ∙

research

∙ 11/26/2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"

This article is the rejoinder for the paper "Probabilistic Integration: ...

18 Francois-Xavier Briol, et al. ∙

research

∙ 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...

0 Robert R. Richardson, et al. ∙

research

∙ 05/27/2018

Contextual Policy Optimisation

Policy gradient methods have been successfully applied to a variety of r...

2 Supratik Paul, et al. ∙

research

∙ 05/22/2018

Optimization, fast and slow: optimally switching between local and Bayesian optimization

We develop the first Bayesian Optimization algorithm, BLOSSOM, which sel...

2 Mark McLeod, et al. ∙

research

∙ 03/28/2018

Quantum algorithms for training Gaussian Processes

Gaussian processes (GPs) are important models in supervised machine lear...

0 Zhikuan Zhao, et al. ∙

research

∙ 03/09/2018

Bayesian Optimization for Dynamic Problems

We propose practical extensions to Bayesian optimization for solving dyn...

0 Favour M. Nyikosa, et al. ∙

research

∙ 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...

0 Robert R. Richardson, et al. ∙

research

∙ 07/13/2017

Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a po...

0 Tom Rainforth, et al. ∙

research

∙ 07/13/2017

Distributionally Ambiguous Optimization Techniques in Batch Bayesian Optimization

We propose a novel, theoretically-grounded, acquisition function for bat...

0 Nikitas Rontsis, et al. ∙

research

∙ 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...

0 Syed Ali Asad Rizvi, et al. ∙

research

∙ 03/23/2017

Distribution of Gaussian Process Arc Lengths

We present the first treatment of the arc length of the Gaussian Process...

0 Justin D. Bewsher, et al. ∙

research

∙ 03/16/2017

Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of b...

0 Robert R. Richardson, et al. ∙

research

∙ 03/13/2017

Practical Bayesian Optimization for Variable Cost Objectives

We propose a novel Bayesian Optimization approach for black-box function...

0 Mark McLeod, et al. ∙

research

∙ 05/24/2016

Alternating Optimisation and Quadrature for Robust Control

Bayesian optimisation has been successfully applied to a variety of rein...

0 Supratik Paul, et al. ∙

research

∙ 02/22/2016

Preconditioning Kernel Matrices

The computational and storage complexity of kernel machines presents the...

0 Kurt Cutajar, et al. ∙

research

∙ 12/03/2015

Probabilistic Integration: A Role in Statistical Computation?

A research frontier has emerged in scientific computation, wherein numer...

0 Francois-Xavier Briol, et al. ∙

research

∙ 10/27/2015

Blitzkriging: Kronecker-structured Stochastic Gaussian Processes

We present Blitzkriging, a new approach to fast inference for Gaussian p...

0 Thomas Nickson, et al. ∙

research

∙ 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...

0 Arnold Salas, et al. ∙

research

∙ 06/08/2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

There is renewed interest in formulating integration as an inference pro...

0 Francois-Xavier Briol, et al. ∙

research

∙ 06/03/2015

Probabilistic Numerics and Uncertainty in Computations

We deliver a call to arms for probabilistic numerical methods: algorithm...

0 Philipp Hennig, et al. ∙

research

∙ 11/03/2014

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

We propose a novel sampling framework for inference in probabilistic mod...

0 Tom Gunter, et al. ∙

research

∙ 11/02/2014

Variational Inference for Gaussian Process Modulated Poisson Processes

We present the first fully variational Bayesian inference scheme for con...

0 Chris Lloyd, et al. ∙

research

∙ 07/30/2014

Automated Machine Learning on Big Data using Stochastic Algorithm Tuning

We introduce a means of automating machine learning (ML) for big data ta...

0 Thomas Nickson, et al. ∙

research

∙ 07/25/2014

Efficient Bayesian Nonparametric Modelling of Structured Point Processes

This paper presents a Bayesian generative model for dependent Cox point ...

0 Tom Gunter, et al. ∙

Michael A. Osborne

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