Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes. - Michael A. Osborne; Stephen J. Roberts; Alex Rogers; Sarvapali D. Ramchurn; Nicholas R. Jennings (IPSN, 2008)

Sequential Bayesian prediction in the presence of changepoints. - Roman Garnett; Michael A. Osborne; Stephen J. Roberts (ICML, 2009)

Active Data Selection for Sensor Networks with Faults and Changepoints. - Michael A. Osborne; Roman Garnett; Stephen J. Roberts (AINA, 2010)

Bayesian optimization for sensor set selection. - Roman Garnett; Michael A. Osborne; Stephen J. Roberts (IPSN, 2010)

Sequential Bayesian Prediction in the Presence of Changepoints and Faults. - Roman Garnett; Michael A. Osborne; Steven Reece; Alex Rogers; Stephen J. Roberts (Comput. J., 2010)

Towards Optimization-Based Multi-Agent Collision-Avoidance Under Continuous Stochastic Dynamics. - Jan-Peter Calliess; Michael Alan Osborne; Stephen J. Roberts (MAPF@AAAI, 2012)

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. - Michael A. Osborne; Roman Garnett; Kevin Swersky; Nando de Freitas (AAAI, 2012)

Can priors be trusted? Learning to anticipate roadworks. - Bonolo Mathibela; Michael A. Osborne; Ingmar Posner; Paul Newman 001 (ITSC, 2012)

Active Learning of Model Evidence Using Bayesian Quadrature. - Michael A. Osborne; David Duvenaud; Roman Garnett; Carl E. Rasmussen; Stephen J. Roberts; Zoubin Ghahramani (NIPS, 2012)

Bayesian Quadrature for Ratios. - Michael A. Osborne; Roman Garnett; Stephen J. Roberts; Christopher Hart; Suzanne Aigrain; Neale Gibson (AISTATS, 2012)

Real-time information processing of environmental sensor network data using bayesian gaussian processes. - Michael A. Osborne; Stephen J. Roberts; Alex Rogers; Nicholas R. Jennings (ACM Trans. Sens. Networks, 2012)

AgentSwitch: towards smart energy tariff selection. - Sarvapali D. Ramchurn; Michael A. Osborne; Oliver Parson; Talal Rahwan; Sasan Maleki; Steven Reece; Trung Dong Huynh; Muddasser Alam; Joel E. Fischer; Tom Rodden; Luc Moreau 001; Stephen J. Roberts (AAMAS, 2013)

Recommending energy tariffs and load shifting based on smart household usage profiling. - Joel E. Fischer; Sarvapali D. Ramchurn; Michael A. Osborne; Oliver Parson; Trung Dong Huynh; Muddasser Alam; Nadia Pantidi; Stuart Moran; Khaled Bachour; Steven Reece; Enrico Costanza; Tom Rodden; et al. (IUI, 2013)

A Kernel for Hierarchical Parameter Spaces. - Frank Hutter; Michael A. Osborne (CoRR, 2013)

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space. - Jan-Peter Calliess; Michael A. Osborne; Stephen J. Roberts (AAMAS, 2014)

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature. - Tom Gunter; Michael A. Osborne; Roman Garnett; Philipp Hennig; Stephen J. Roberts (NIPS, 2014)

Active Learning of Linear Embeddings for Gaussian Processes. - Roman Garnett; Michael A. Osborne; Philipp Hennig (UAI, 2014)

Efficient Bayesian Nonparametric Modelling of Structured Point Processes. - Tom Gunter; Chris M. Lloyd; Michael A. Osborne; Stephen J. Roberts (UAI, 2014)

Communication Communities in MOOCs. - Nabeel Gillani; Rebecca Eynon; Michael A. Osborne; Isis Hjorth; Stephen J. Roberts (CoRR, 2014)

Variational Inference for Gaussian Process Modulated Poisson Processes. - Chris M. Lloyd; Tom Gunter; Michael A. Osborne; Stephen J. Roberts (ICML, 2015)

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne (NIPS, 2015)

Probabilistic Integration. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2015)

Probabilistic Numerics and Uncertainty in Computations. - Philipp Hennig; Michael A. Osborne; Mark A. Girolami (CoRR, 2015)

GLASSES: Relieving The Myopia Of Bayesian Optimisation. - Javier González 002; Michael A. Osborne; Neil D. Lawrence (AISTATS, 2016)

Latent Point Process Allocation. - Chris M. Lloyd; Tom Gunter; Michael A. Osborne; Stephen J. Roberts; Tom Nickson (AISTATS, 2016)

Preconditioning Kernel Matrices. - Kurt Cutajar; Michael A. Osborne; John P. Cunningham; Maurizio Filippone (ICML, 2016)

Bayesian Optimization for Probabilistic Programs. - Tom Rainforth; Tuan Anh Le 001; Jan-Willem van de Meent; Michael A. Osborne; Frank D. Wood (NIPS, 2016)

Alternating Optimisation and Quadrature for Robust Reinforcement Learning. - Supratik Paul; Kamil Ciosek; Michael A. Osborne; Shimon Whiteson (CoRR, 2016)

Identifying Sources of Discrimination Risk in the Life Cycle of Machine Intelligence Applications under New European Union Regulations. - Syed Ali Asad Rizvi; Elmarie van Heerden; Arnold Salas; Favour Nyikosa; Stephen J. Roberts; Michael A. Osborne; Elmer Rodriguez (AAAI Spring Symposia, 2017)

Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer's Disease. - Wolfgang Fruehwirt; Pengfei Zhang 001; Matthias Gerstgrasser; Dieter Grossegger; Reinhold Schmidt; Thomas Benke; Peter Dal-Bianco; Gerhard Ransmayr; Leonard Weydemann; Heinrich Garn; Markus Waser; Michael A. Osborne; et al. (AIME, 2017)

Distribution of Gaussian Process Arc Lengths. - Justin Bewsher; Alessandra Tosi; Michael A. Osborne; Stephen J. Roberts (AISTATS, 2017)

Entropic Trace Estimates for Log Determinants. - Jack K. Fitzsimons; Diego Granziol; Kurt Cutajar; Michael A. Osborne; Maurizio Filippone; Stephen J. Roberts (ECML/PKDD (1), 2017)

Bayesian Inference of Log Determinants. - Jack K. Fitzsimons; Kurt Cutajar; Maurizio Filippone; Michael A. Osborne; Stephen J. Roberts (UAI, 2017)

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes. - Syed Ali Asad Rizvi; Stephen J. Roberts; Michael A. Osborne; Favour Nyikosa (CoRR, 2017)

Alternating Optimisation and Quadrature for Robust Control. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (AAAI, 2018)

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. - Gabriele Abbati; Alessandra Tosi; Michael A. Osborne; Seth R. Flaxman (AISTATS, 2018)

Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization. - Mark McLeod; Stephen J. Roberts; Michael A. Osborne (ICML, 2018)

Fast Information-theoretic Bayesian Optimisation. - Bin Xin Ru; Mark McLeod; Diego Granziol; Michael A. Osborne (ICML, 2018)

Improved Stochastic Trace Estimation using Mutually Unbiased Bases. - Jack K. Fitzsimons; Michael A. Osborne; Stephen J. Roberts; Joseph Francis Fitzsimons (UAI, 2018)

VBALD - Variational Bayesian Approximation of Log Determinants. - Diego Granziol; Edward Wagstaff; Bin Xin Ru; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Quantum algorithms for training Gaussian Processes. - Zhikuan Zhao; Jack K. Fitzsimons; Michael A. Osborne; Stephen J. Roberts; Joseph F. Fitzsimons (CoRR, 2018)

Entropic Spectral Learning in Large Scale Networks. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Contextual Policy Optimisation. - Supratik Paul; Michael A. Osborne; Shimon Whiteson (CoRR, 2018)

Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness. - Jack K. Fitzsimons; AbdulRahman Al Ali; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

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

Intersectionality: Multiple Group Fairness in Expectation Constraints. - Jack K. Fitzsimons; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2018)

Batch Selection for Parallelisation of Bayesian Quadrature. - Edward Wagstaff; Saad Hamid; Michael A. Osborne (CoRR, 2018)

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting. - Pengfei Zhang 001; Ido Nevat; Gareth W. Peters; François Septier; Michael A. Osborne (IEEE Trans. Signal Process., 2018)

Inferring Work Task Automatability from AI Expert Evidence. - Paul Duckworth; Logan Graham; Michael A. Osborne (AIES, 2019)

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. - Gabriele Abbati; Philippe Wenk; Michael A. Osborne; Andreas Krause 001; Bernhard Schölkopf; Stefan Bauer (ICML, 2019)

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. - Ahsan S. Alvi; Bin Xin Ru; Jan-Peter Calliess; Stephen J. Roberts; Michael A. Osborne (ICML, 2019)

Automated Model Selection with Bayesian Quadrature. - Henry Chai; Jean-Francois Ton; Michael A. Osborne; Roman Garnett (ICML, 2019)

Fingerprint Policy Optimisation for Robust Reinforcement Learning. - Supratik Paul; Michael A. Osborne; Shimon Whiteson (ICML, 2019)

On the Limitations of Representing Functions on Sets. - Edward Wagstaff; Fabian Fuchs; Martin Engelcke; Ingmar Posner; Michael A. Osborne (ICML, 2019)

Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. - Sebastian Farquhar; Michael A. Osborne; Yarin Gal (CoRR, 2019)

Adaptive Configuration Oracle for Online Portfolio Selection Methods. - Favour M. Nyikosa; Michael A. Osborne; Stephen J. Roberts (CoRR, 2019)

A Maximum Entropy approach to Massive Graph Spectra. - Diego Granziol; Robin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2019)

A General Framework for Fair Regression. - Jack K. Fitzsimons; AbdulRahman Al Ali; Michael A. Osborne; Stephen J. Roberts (Entropy, 2019)

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (Entropy, 2019)

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries. - Robert R. Richardson; Christoph R. Birkl; Michael A. Osborne; David A. Howey (IEEE Trans. Ind. Informatics, 2019)

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. - Philippe Wenk; Gabriele Abbati; Michael A. Osborne; Bernhard Schölkopf; Andreas Krause 001; Stefan Bauer (AAAI, 2020)

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. - Sebastian Farquhar; Michael A. Osborne; Yarin Gal (AISTATS, 2020)

Knowing The What But Not The Where in Bayesian Optimization. - Vu Nguyen; Michael A. Osborne (ICML, 2020)

Bayesian Optimisation over Multiple Continuous and Categorical Inputs. - Bin Xin Ru; Ahsan S. Alvi; Vu Nguyen; Michael A. Osborne; Stephen J. Roberts (ICML, 2020)

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. - Vu Nguyen; Vaden Masrani; Rob Brekelmans; Michael A. Osborne; Frank Wood (NeurIPS, 2020)

Bayesian Optimization for Iterative Learning. - Vu Nguyen; Sebastian Schulze; Michael A. Osborne (NeurIPS, 2020)

Machine learning enables completely automatic tuning of a quantum device faster than human experts. - 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; et al. (CoRR, 2020)

Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel. - Bin Xin Ru; Xingchen Wan; Xiaowen Dong 001; Michael A. Osborne (CoRR, 2020)

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

Gaussian Process Bandit Optimization of theThermodynamic Variational Objective. - Vu Nguyen; Vaden Masrani; Rob Brekelmans; Michael A. Osborne; Frank Wood (CoRR, 2020)

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Distributionally Ambiguous Optimization for Batch Bayesian Optimization. - Nikitas Rontsis; Michael A. Osborne; Paul J. Goulart (J. Mach. Learn. Res., 2020)

Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. - Bin Xin Ru; Xingchen Wan; Xiaowen Dong 001; Michael A. Osborne (ICLR, 2021)

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. - Vu Nguyen; Tam Le; Makoto Yamada; Michael A. Osborne (ICML, 2021)

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. - Xingchen Wan; Vu Nguyen; Huong Ha 001; Bin Xin Ru; Cong Lu; Michael A. Osborne (ICML, 2021)

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. - Tim G. J. Rudner; Cong Lu; Michael A. Osborne; Yarin Gal; Yee Whye Teh (NeurIPS, 2021)

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Robin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (NeurIPS, 2021)

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. - Brandon Severin; Dominic T. Lennon; Leon C. Camenzind; Florian Vigneau; Federico Fedele; D. Jirovec; A. Ballabio; D. Chrastina; G. Isella; M. de Kruijf; Miguel J. Carballido; Simon Svab; et al. (CoRR, 2021)

Revisiting Design Choices in Model-Based Offline Reinforcement Learning. - Cong Lu; Philip J. Ball; Jack Parker-Holder; Michael A. Osborne; Stephen J. Roberts (CoRR, 2021)

Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. - Vu Nguyen; Marc Peter Deisenroth; Michael A. Osborne (CoRR, 2021)

Adversarial Attacks on Graph Classification via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Binxin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (CoRR, 2021)

Bridging the reality gap in quantum devices with physics-aware machine learning. - David L. Craig; H. Moon; Federico Fedele; Dominic T. Lennon; Barnaby van Straaten; Florian Vigneau; Leon C. Camenzind; Dominik M. Zumbühl; G. Andrew D. Briggs; Michael A. Osborne; Dino Sejdinovic; Natalia Ares (CoRR, 2021)

Personalized brain stimulation for effective neurointervention across participants. - Nienke E. R. van Bueren; Thomas L. Reed; Vu Nguyen 001; James G. Sheffield; Sanne H. G. van der Ven; Michael A. Osborne; Evelyn H. Kroesbergen; Roi Cohen Kadosh (PLoS Comput. Biol., 2021)

Marginalising over Stationary Kernels with Bayesian Quadrature. - Saad Hamid; Sebastian Schulze; Michael A. Osborne; Stephen J. Roberts (AISTATS, 2022)

Bayesian Generational Population-Based Training. - Xingchen Wan; Cong Lu; Jack Parker-Holder; Philip J. Ball; Vu Nguyen; Binxin Ru; Michael A. Osborne (AutoML, 2022)

Revisiting Design Choices in Offline Model Based Reinforcement Learning. - Cong Lu; Philip J. Ball; Jack Parker-Holder; Michael A. Osborne; Stephen J. Roberts (ICLR, 2022)

Robust Multi-Objective Bayesian Optimization Under Input Noise. - Samuel Daulton; Sait Cakmak; Maximilian Balandat; Michael A. Osborne; Enlu Zhou; Eytan Bakshy (ICML, 2022)

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (NeurIPS, 2022)

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. - Michael K. Cohen; Samuel Daulton; Michael A. Osborne (NeurIPS, 2022)

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. - Samuel Daulton; Xingchen Wan; David Eriksson; Maximilian Balandat; Michael A. Osborne; Eytan Bakshy (NeurIPS, 2022)

Bezier Gaussian Processes for Tall and Wide Data. - Martin Jørgensen; Michael A. Osborne (NeurIPS, 2022)

Advanced Artificial Agents Intervene in the Provision of Reward. - Michael K. Cohen; Marcus Hutter; Michael A. Osborne (AI Mag., 2022)

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. - Diego Granziol; Binxin Ru; Xiaowen Dong 001; Stefan Zohren; Michael A. Osborne; Stephen J. Roberts (Algorithms, 2022)

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature. - Masaki Adachi; Yannick Kuhn; Birger Horstmann; Michael A. Osborne; David A. Howey (CoRR, 2022)

Universal Approximation of Functions on Sets. - Edward Wagstaff; Fabian B. Fuchs; Martin Engelcke; Michael A. Osborne; Ingmar Posner (J. Mach. Learn. Res., 2022)

Bayesian Optimisation of Functions on Graphs. - Xingchen Wan; Pierre Osselin; Henry Kenlay; Binxin Ru; Michael A. Osborne; Xiaowen Dong 001 (NeurIPS, 2023)

SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints. - Masaki Adachi; Satoshi Hayakawa; Saad Hamid; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (CoRR, 2023)

Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature. - Masaki Adachi; Satoshi Hayakawa; Xingchen Wan; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (CoRR, 2023)

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Krikamol Muandet; Michael A. Osborne; Siu Lun Chau (CoRR, 2023)

Bayesian Quadrature for Neural Ensemble Search. - Saad Hamid; Xingchen Wan; Martin Jørgensen; Binxin Ru; Michael A. Osborne (Trans. Mach. Learn. Res., 2023)

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. - Cong Lu; Philip J. Ball; Tim G. J. Rudner; Jack Parker-Holder; Michael A. Osborne; Yee Whye Teh (Trans. Mach. Learn. Res., 2023)

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Xingchen Wan; Vu Nguyen; Harald Oberhauser; Michael A. Osborne (AISTATS, 2024)

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Michael A. Osborne; Sebastian Orbell; Natalia Ares; Krikamol Muandet; Siu Lun Chau (AISTATS, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - 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; et al. (ICML, 2024)

Principled Bayesian Optimization in Collaboration with Human Experts. - Wenjie Xu; Masaki Adachi; Colin N. Jones; Michael A. Osborne (NeurIPS, 2024)

Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal. - Juliusz Ziomek; Masaki Adachi; Michael A. Osborne (NeurIPS, 2024)

Walking the Values in Bayesian Inverse Reinforcement Learning. - Ondrej Bajgar; Alessandro Abate; Konstantinos Gatsis; Michael A. Osborne (UAI, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - 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; et al. (CoRR, 2024)

Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type. - Juliusz Ziomek; Masaki Adachi; Michael A. Osborne (CoRR, 2024)

Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation. - Lennart Heim; Timothy Fist; Janet Egan; Sihao Huang; Stephen A. Zekany; Robert Trager; Michael A. Osborne; Noa Zilberman (CoRR, 2024)

A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Saad Hamid; Harald Oberhauser; Michael A. Osborne (CoRR, 2024)

Principled Bayesian Optimisation in Collaboration with Human Experts. - Wenjie Xu; Masaki Adachi; Colin N. Jones; Michael A. Osborne (CoRR, 2024)

Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes. - Csaba Tóth; Masaki Adachi; Michael A. Osborne; Harald Oberhauser (AISTATS, 2025)

Time-varying Gaussian Process Bandits with Unknown Prior. - Juliusz Ziomek; Masaki Adachi; Michael A. Osborne (AISTATS, 2025)

Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation. - George Whittle; Juliusz Ziomek; Jacob Rawling; Michael A. Osborne (CoRR, 2025)

Bayesian Optimization for Building Social-Influence-Free Consensus. - Masaki Adachi; Siu Lun Chau; Wenjie Xu; Anurag Singh 006; Michael A. Osborne; Krikamol Muandet (CoRR, 2025)

Position: Ensuring mutual privacy is necessary for effective external evaluation of proprietary AI systems. - Ben Bucknall; Robert F. Trager; Michael A. Osborne (CoRR, 2025)

Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature. - Masaki Adachi; Masahiro Fujisawa; Michael A. Osborne (CoRR, 2025)

Meta-learning characteristics and dynamics of quantum systems. - Lucas Schorling; Pranav Vaidhyanathan; Jonas Schuff; Miguel J. Carballido; Dominik M. Zumbühl; Gerard Milburn; Florian Marquardt; Jakob N. Foerster; Michael A. Osborne; Natalia Ares (CoRR, 2025)

A Physics-Inspired Optimizer: Velocity Regularized Adam. - Pranav Vaidhyanathan; Lucas Schorling; Natalia Ares; Michael A. Osborne (CoRR, 2025)

Just One Layer Norm Guarantees Stable Extrapolation. - Juliusz Ziomek; George Whittle; Michael A. Osborne (CoRR, 2025)

Scalable Valuation of Human Feedback through Provably Robust Model Alignment. - Masahiro Fujisawa; Masaki Adachi; Michael A. Osborne (CoRR, 2025)

SOReL and TOReL: Two Methods for Fully Offline Reinforcement Learning. - Mattie Fellows; Clarisse Wibault; Uljad Berdica; Johannes Forkel; Michael A. Osborne; Jakob N. Foerster (CoRR, 2025)

Natural Evolutionary Search meets Probabilistic Numerics. - Pierre Osselin; Masaki Adachi; Xiaowen Dong 001; Michael A. Osborne (CoRR, 2025)