Michael A. Osborne

Orcid: 0000-0003-1959-012X

Affiliations:
  • University of Oxford, UK


According to our database1, Michael A. Osborne authored at least 112 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Principled Bayesian Optimisation in Collaboration with Human Experts.
CoRR, 2024

Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal.
CoRR, 2024

Walking the Values in Bayesian Inverse Reinforcement Learning.
CoRR, 2024

A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting.
CoRR, 2024

Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation.
CoRR, 2024

Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024


Looping in the Human: Collaborative and Explainable Bayesian Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations.
Trans. Mach. Learn. Res., 2023

Bayesian Quadrature for Neural Ensemble Search.
Trans. Mach. Learn. Res., 2023

Looping in the Human: Collaborative and Explainable Bayesian Optimization.
CoRR, 2023

Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature.
CoRR, 2023

SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints.
CoRR, 2023

Bayesian Optimisation of Functions on Graphs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Universal Approximation of Functions on Sets.
J. Mach. Learn. Res., 2022

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature.
CoRR, 2022

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications.
Algorithms, 2022

Advanced Artificial Agents Intervene in the Provision of Reward.
AI Mag., 2022

Bezier Gaussian Processes for Tall and Wide Data.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Multi-Objective Bayesian Optimization Under Input Noise.
Proceedings of the International Conference on Machine Learning, 2022

Revisiting Design Choices in Offline Model Based Reinforcement Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Bayesian Generational Population-Based Training.
Proceedings of the International Conference on Automated Machine Learning, 2022

Marginalising over Stationary Kernels with Bayesian Quadrature.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Personalized brain stimulation for effective neurointervention across participants.
PLoS Comput. Biol., 2021

Bridging the reality gap in quantum devices with physics-aware machine learning.
CoRR, 2021

Adversarial Attacks on Graph Classification via Bayesian Optimisation.
CoRR, 2021

Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds.
CoRR, 2021

Revisiting Design Choices in Model-Based Offline Reinforcement Learning.
CoRR, 2021

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning.
CoRR, 2021

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces.
Proceedings of the 38th International Conference on Machine Learning, 2021

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search.
Proceedings of the 38th International Conference on Machine Learning, 2021

Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Distributionally Ambiguous Optimization for Batch Bayesian Optimization.
J. Mach. Learn. Res., 2020

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature.
J. Mach. Learn. Res., 2020

Gaussian Process Bandit Optimization of theThermodynamic Variational Objective.
CoRR, 2020

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices.
CoRR, 2020

Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel.
CoRR, 2020

Machine learning enables completely automatic tuning of a quantum device faster than human experts.
CoRR, 2020

Bayesian Optimization for Iterative Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bayesian Optimisation over Multiple Continuous and Categorical Inputs.
Proceedings of the 37th International Conference on Machine Learning, 2020

Knowing The What But Not The Where in Bayesian Optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries.
IEEE Trans. Ind. Informatics, 2019

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning.
Entropy, 2019

A General Framework for Fair Regression.
Entropy, 2019

A Maximum Entropy approach to Massive Graph Spectra.
CoRR, 2019

Adaptive Configuration Oracle for Online Portfolio Selection Methods.
CoRR, 2019

Radial Bayesian Neural Networks: Robust Variational Inference In Big Models.
CoRR, 2019

On the Limitations of Representing Functions on Sets.
Proceedings of the 36th International Conference on Machine Learning, 2019

Fingerprint Policy Optimisation for Robust Reinforcement Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Automated Model Selection with Bayesian Quadrature.
Proceedings of the 36th International Conference on Machine Learning, 2019

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation.
Proceedings of the 36th International Conference on Machine Learning, 2019

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs.
Proceedings of the 36th International Conference on Machine Learning, 2019

Inferring Work Task Automatability from AI Expert Evidence.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting.
IEEE Trans. Signal Process., 2018

Batch Selection for Parallelisation of Bayesian Quadrature.
CoRR, 2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?".
CoRR, 2018

Intersectionality: Multiple Group Fairness in Expectation Constraints.
CoRR, 2018

Efficiently measuring a quantum device using machine learning.
CoRR, 2018

Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness.
CoRR, 2018

Contextual Policy Optimisation.
CoRR, 2018

Entropic Spectral Learning in Large Scale Networks.
CoRR, 2018

Quantum algorithms for training Gaussian Processes.
CoRR, 2018

VBALD - Variational Bayesian Approximation of Log Determinants.
CoRR, 2018

Improved Stochastic Trace Estimation using Mutually Unbiased Bases.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Fast Information-theoretic Bayesian Optimisation.
Proceedings of the 35th International Conference on Machine Learning, 2018

Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Alternating Optimisation and Quadrature for Robust Control.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes.
CoRR, 2017

Bayesian Inference of Log Determinants.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Entropic Trace Estimates for Log Determinants.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Distribution of Gaussian Process Arc Lengths.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer's Disease.
Proceedings of the Artificial Intelligence in Medicine, 2017

Identifying Sources of Discrimination Risk in the Life Cycle of Machine Intelligence Applications under New European Union Regulations.
Proceedings of the 2017 AAAI Spring Symposia, 2017

2016
Alternating Optimisation and Quadrature for Robust Reinforcement Learning.
CoRR, 2016

Bayesian Optimization for Probabilistic Programs.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Preconditioning Kernel Matrices.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Latent Point Process Allocation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

GLASSES: Relieving The Myopia Of Bayesian Optimisation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Probabilistic Numerics and Uncertainty in Computations.
CoRR, 2015

Probabilistic Integration.
CoRR, 2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Inference for Gaussian Process Modulated Poisson Processes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Communication Communities in MOOCs.
CoRR, 2014

Efficient Bayesian Nonparametric Modelling of Structured Point Processes.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Active Learning of Linear Embeddings for Gaussian Processes.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

2013
A Kernel for Hierarchical Parameter Spaces.
CoRR, 2013

Recommending energy tariffs and load shifting based on smart household usage profiling.
Proceedings of the 18th International Conference on Intelligent User Interfaces, 2013

AgentSwitch: towards smart energy tariff selection.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2013

2012
Real-time information processing of environmental sensor network data using bayesian gaussian processes.
ACM Trans. Sens. Networks, 2012

Bayesian Quadrature for Ratios.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Active Learning of Model Evidence Using Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Can priors be trusted? Learning to anticipate roadworks.
Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, 2012

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

Towards Optimization-Based Multi-Agent Collision-Avoidance Under Continuous Stochastic Dynamics.
Proceedings of the Multiagent Pathfinding, Papers from the 2012 AAAI Workshop, 2012

2010
Sequential Bayesian Prediction in the Presence of Changepoints and Faults.
Comput. J., 2010

Bayesian optimization for sensor set selection.
Proceedings of the 9th International Conference on Information Processing in Sensor Networks, 2010

Active Data Selection for Sensor Networks with Faults and Changepoints.
Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 2010

2009
Sequential Bayesian prediction in the presence of changepoints.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes.
Proceedings of the 7th International Conference on Information Processing in Sensor Networks, 2008


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