Michael A. Osborne

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 118

Top Venues

Publications

Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau
Looping in the Human: Collaborative and Explainable Bayesian Optimization. AISTATS (2024)
Juliusz Ziomek, Masaki Adachi, Michael A. Osborne
Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type. CoRR (2024)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. AISTATS (2024)
Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael A. Osborne
Walking the Values in Bayesian Inverse Reinforcement Learning. CoRR (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Lennart Heim, Tim Fist, Janet Egan, Sihao Huang, Stephen Zekany, Robert Trager, Michael A. Osborne, Noa Zilberman
Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation. CoRR (2024)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Saad Hamid, Harald Oberhauser, Michael A. Osborne
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting. CoRR (2024)
Masaki Adachi, Satoshi Hayakawa, Saad Hamid, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne
SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints. CoRR (2023)
Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong
Bayesian Optimisation of Functions on Graphs. CoRR (2023)
Masaki Adachi, Satoshi Hayakawa, Xingchen Wan, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature. CoRR (2023)
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. Trans. Mach. Learn. Res. 2023 (2023)
Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau
Looping in the Human: Collaborative and Explainable Bayesian Optimization. CoRR (2023)
Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong
Bayesian Optimisation of Functions on Graphs. NeurIPS (2023)
Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne
Bayesian Quadrature for Neural Ensemble Search. Trans. Mach. Learn. Res. 2023 (2023)
Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne
Bayesian Quadrature for Neural Ensemble Search. CoRR (2023)
Michael K. Cohen, Samuel Daulton, Michael A. Osborne
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. NeurIPS (2022)
Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts
Marginalising over Stationary Kernels with Bayesian Quadrature. AISTATS (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. AutoML (2022)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. CoRR (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. CoRR (2022)
Masaki Adachi, Yannick Kuhn, Birger Horstmann, Michael A. Osborne, David A. Howey
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature. CoRR (2022)
Diego Granziol, Binxin Ru, Xiaowen Dong, Stefan Zohren, Michael A. Osborne, Stephen J. Roberts
Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. Algorithms 15 (6) (2022)
Martin Jørgensen, Michael A. Osborne
Bezier Gaussian Processes for Tall and Wide Data. NeurIPS (2022)
Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts
Revisiting Design Choices in Offline Model Based Reinforcement Learning. ICLR (2022)
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner
Universal Approximation of Functions on Sets. J. Mach. Learn. Res. 23 (2022)
Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS (2022)
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. CoRR (2022)
Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML (2022)
Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR (2022)
Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR (2022)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR (2022)
Michael K. Cohen, Marcus Hutter, Michael A. Osborne
Advanced Artificial Agents Intervene in the Provision of Reward. AI Mag. 43 (3) (2022)
Michael K. Cohen, Samuel Daulton, Michael A. Osborne
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. CoRR (2022)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. NeurIPS (2022)
Martin Jørgensen, Michael A. Osborne
Bézier Gaussian Processes for Tall and Wide Data. CoRR (2022)
Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS (2021)
Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts
Marginalising over Stationary Kernels with Bayesian Quadrature. CoRR (2021)
Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong
Adversarial Attacks on Graph Classification via Bayesian Optimisation. CoRR (2021)
Nienke E. R. van Bueren, Thomas L. Reed, Vu Nguyen, James G. Sheffield, Sanne H. G. van der Ven, Michael A. Osborne, Evelyn H. Kroesbergen, Roi Cohen Kadosh
Personalized brain stimulation for effective neurointervention across participants. PLoS Comput. Biol. 17 (9) (2021)
Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. CoRR (2021)
B. Severin, Dominic T. Lennon, Leon C. Camenzind, Florian Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, Andreas V. Kuhlmann, F. R. Braakman, Simon Geyer, F. N. M. Froning, H. Moon, Michael A. Osborne, Dino Sejdinovic, G. Katsaros, Dominik M. Zumbühl, G. Andrew D. Briggs, Natalia Ares
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. CoRR (2021)
Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts
Revisiting Design Choices in Model-Based Offline Reinforcement Learning. CoRR (2021)
Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. ICML (2021)
David L. Craig, H. Moon, Federico Fedele, Dominic T. Lennon, B. Van Straaten, Florian Vigneau, Leon C. Camenzind, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares
Bridging the reality gap in quantum devices with physics-aware machine learning. CoRR (2021)
Xingchen Wan, Vu Nguyen, Huong Ha, Bin Xin Ru, Cong Lu, Michael A. Osborne
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. ICML (2021)
Xingchen Wan, Vu Nguyen, Huong Ha, Bin Xin Ru, Cong Lu, Michael A. Osborne
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. CoRR (2021)
Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. NeurIPS (2021)
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner
Universal Approximation of Functions on Sets. CoRR (2021)
Bin Xin Ru, Xingchen Wan, Xiaowen Dong, Michael A. Osborne
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. ICLR (2021)
Supratik Paul, Konstantinos I. Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson
Robust Reinforcement Learning with Bayesian Optimisation and Quadrature. J. Mach. Learn. Res. 21 (2020)
Sebastian Farquhar, Michael A. Osborne, Yarin Gal
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. AISTATS (2020)
Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. CoRR (2020)
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. NeurIPS (2020)
Vu Nguyen, Michael A. Osborne
Knowing The What But Not The Where in Bayesian Optimization. ICML (2020)
Bin Xin Ru, Xingchen Wan, Xiaowen Dong, Michael A. Osborne
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel. CoRR (2020)
Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. AAAI (2020)
Bin Xin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J. Roberts
Bayesian Optimisation over Multiple Continuous and Categorical Inputs. ICML (2020)
Vu Nguyen, Sebastian Schulze, Michael A. Osborne
Bayesian Optimization for Iterative Learning. NeurIPS (2020)
Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart
Distributionally Ambiguous Optimization for Batch Bayesian Optimization. J. Mach. Learn. Res. 21 (2020)
V. Nguyen, S. B. Orbell, Dominic T. Lennon, Hyungil Moon, Florian Vigneau, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. CoRR (2020)
Hyungil Moon, Dominic T. Lennon, James Kirkpatrick, Nina M. van Esbroeck, Leon C. Camenzind, Liuqi Yu, Florian Vigneau, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Edward A. Laird, Natalia Ares
Machine learning enables completely automatic tuning of a quantum device faster than human experts. CoRR (2020)
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood
Gaussian Process Bandit Optimization of theThermodynamic Variational Objective. CoRR (2020)
Vu Nguyen, Sebastian Schulze, Michael A. Osborne
Bayesian Optimization for Iterative Learning. CoRR (2019)
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne
On the Limitations of Representing Functions on Sets. ICML (2019)
Philippe Wenk, Gabriele Abbati, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. CoRR (2019)
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne
On the Limitations of Representing Functions on Sets. CoRR (2019)
Robert R. Richardson, Christoph R. Birkl, Michael A. Osborne, David A. Howey
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries. IEEE Trans. Ind. Informatics 15 (1) (2019)
Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts
Adaptive Configuration Oracle for Online Portfolio Selection Methods. CoRR (2019)
Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
A Maximum Entropy approach to Massive Graph Spectra. CoRR (2019)
Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett
Automated Model Selection with Bayesian Quadrature. ICML (2019)
Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. Entropy 21 (6) (2019)
Paul Duckworth, Logan Graham, Michael A. Osborne
Inferring Work Task Automatability from AI Expert Evidence. AIES (2019)
Gabriele Abbati, Philippe Wenk, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs. CoRR (2019)
Supratik Paul, Michael A. Osborne, Shimon Whiteson
Fingerprint Policy Optimisation for Robust Reinforcement Learning. ICML (2019)
Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. CoRR (2019)
Sebastian Farquhar, Michael A. Osborne, Yarin Gal
Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. CoRR (2019)
Ahsan S. Alvi, Bin Xin Ru, Jan Calliess, Stephen J. Roberts, Michael A. Osborne
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. CoRR (2019)
Henry Chai, Jean-Francois Ton, Roman Garnett, Michael A. Osborne
Automated Model Selection with Bayesian Quadrature. CoRR (2019)
Vu Nguyen, Michael A. Osborne
Knowing The What But Not The Where in Bayesian Optimization. CoRR (2019)
Jack K. Fitzsimons, AbdulRahman Al Ali, Michael A. Osborne, Stephen J. Roberts
A General Framework for Fair Regression. Entropy 21 (8) (2019)
Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. ICML (2019)
Bin Xin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J. Roberts
Bayesian Optimisation over Multiple Continuous and Categorical Inputs. CoRR (2019)
Ahsan S. Alvi, Bin Xin Ru, Jan-Peter Calliess, Stephen J. Roberts, Michael A. Osborne
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. ICML (2019)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR (2018)
Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph Francis Fitzsimons
Improved Stochastic Trace Estimation using Mutually Unbiased Bases. UAI (2018)
Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts
Entropic Spectral Learning in Large Scale Networks. CoRR (2018)
Jack K. Fitzsimons, AbdulRahman Al Ali, Michael A. Osborne, Stephen J. Roberts
Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness. CoRR (2018)
Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts
Intersectionality: Multiple Group Fairness in Expectation Constraints. CoRR (2018)
Mark McLeod, Michael A. Osborne, Stephen J. Roberts
Optimization, fast and slow: optimally switching between local and Bayesian optimization. CoRR (2018)
Gabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. AISTATS (2018)
Supratik Paul, Konstantinos I. Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson
Alternating Optimisation and Quadrature for Robust Control. AAAI (2018)
Edward Wagstaff, Saad Hamid, Michael A. Osborne
Batch Selection for Parallelisation of Bayesian Quadrature. CoRR (2018)
Bin Xin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne
Fast Information-theoretic Bayesian Optimisation. ICML (2018)
Mark McLeod, Stephen J. Roberts, Michael A. Osborne
Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization. ICML (2018)
Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph F. Fitzsimons
Quantum algorithms for training Gaussian Processes. CoRR (2018)
Dominic T. Lennon, Hyungil Moon, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Edward A. Laird, Natalia Ares
Efficiently measuring a quantum device using machine learning. CoRR (2018)
Pengfei Zhang, Ido Nevat, Gareth W. Peters, François Septier, Michael A. Osborne
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting. IEEE Trans. Signal Process. 66 (9) (2018)
Supratik Paul, Michael A. Osborne, Shimon Whiteson
Contextual Policy Optimisation. CoRR (2018)
Diego Granziol, Edward Wagstaff, Bin Xin Ru, Michael A. Osborne, Stephen J. Roberts
VBALD - Variational Bayesian Approximation of Log Determinants. CoRR (2018)
Justin Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts
Distribution of Gaussian Process Arc Lengths. AISTATS (2017)

Michael A. Osborne.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Michael A. Osborne