Search Results for author: Michael Osborne

Found 22 papers, 7 papers with code

Bayesian Quadrature for Neural Ensemble Search

1 code implementation15 Mar 2023 Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael Osborne

Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks.

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation

1 code implementation NeurIPS 2021 Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael Osborne, Xiaowen Dong

While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.

Adversarial Robustness Bayesian Optimisation +1

Revisiting Design Choices in Offline Model Based Reinforcement Learning

no code implementations NeurIPS 2021 Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, S Roberts

Offline reinforcement learning enables agents to make use of large pre-collected datasets of environment transitions and learn control policies without the need for potentially expensive or unsafe online data collection.

Bayesian Optimization Model-based Reinforcement Learning +3

Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels

1 code implementation ICLR 2021 Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne

Our method optimises the architecture in a highly data-efficient manner: it is capable of capturing the topological structures of the architectures and is scalable to large graphs, thus making the high-dimensional and graph-like search spaces amenable to BO.

Bayesian Optimisation Neural Architecture Search

A Maximum Entropy approach to Massive Graph Spectra

no code implementations19 Dec 2019 Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts

Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing.

Graph Similarity

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

4 code implementations1 Jul 2019 Sebastian Farquhar, Michael Osborne, Yarin Gal

The Radial BNN is motivated by avoiding a sampling problem in 'mean-field' variational inference (MFVI) caused by the so-called 'soap-bubble' pathology of multivariate Gaussians.

Continual Learning Variational Inference

On the Limitations of Representing Functions on Sets

no code implementations25 Jan 2019 Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne

Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance.

Gaussian Processes

Batch Selection for Parallelisation of Bayesian Quadrature

1 code implementation4 Dec 2018 Ed Wagstaff, Saad Hamid, Michael Osborne

Integration over non-negative integrands is a central problem in machine learning (e. g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions).

Bayesian Optimisation BIG-bench Machine Learning +1

A General Framework for Fair Regression

no code implementations10 Oct 2018 Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, Stephen Roberts

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community.

Fairness Gaussian Processes +1

Entropic Spectral Learning for Large-Scale Graphs

no code implementations18 Apr 2018 Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts

Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network.

Community Detection

Entropic Trace Estimates for Log Determinants

1 code implementation24 Apr 2017 Jack Fitzsimons, Diego Granziol, Kurt Cutajar, Michael Osborne, Maurizio Filippone, Stephen Roberts

The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.

Gaussian Processes Point Processes

Bayesian Inference of Log Determinants

no code implementations5 Apr 2017 Jack Fitzsimons, Kurt Cutajar, Michael Osborne, Stephen Roberts, Maurizio Filippone

The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.

Bayesian Inference Gaussian Processes +1

GLASSES: Relieving The Myopia Of Bayesian Optimisation

no code implementations21 Oct 2015 Javier González, Michael Osborne, Neil D. Lawrence

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search.

Bayesian Optimisation

Communication Communities in MOOCs

no code implementations18 Mar 2014 Nabeel Gillani, Rebecca Eynon, Michael Osborne, Isis Hjorth, Stephen Roberts

Massive Open Online Courses (MOOCs) bring together thousands of people from different geographies and demographic backgrounds -- but to date, little is known about how they learn or communicate.

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

no code implementations17 Feb 2014 Jan-Peter Calliess, Michael Osborne, Stephen Roberts

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic.

Collision Avoidance

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