Michael A Osborne
15 papers · 2014–2025 · 3 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (29) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Topic Evolution
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Mega-Team · Squad
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Keyword Collector
(68)
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Trend Setter
💎
Century Club · Bronze
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Unstoppable · Bronze
Venues
Top co-authors
Research topics
Machine Learning
(13)
Artificial Intelligence
(9)
Bayesian & Probabilistic · Artificial Intelligence
(8)
Bayesian & Probabilistic · Machine Learning
(8)
Optimization & Theory
(8)
Mathematics & Optimization
(6)
Optimization
(6)
Learning Types
(4)
Core Methods
(3)
Deep Learning
(2)
Core AI
(2)
Reinforcement Learning
(1)
Applications
(1)
Architectures
(1)
Learning Paradigms
(1)
Keywords
bayesian optimization
(7)
gaussian process
(5)
bayesian quadrature
(3)
kernel methods
(3)
acquisition function
(2)
active learning
(2)
regret bound
(2)
black-box optimization
(2)
surrogate model
(2)
numerical integration
(2)
imitation learning
(1)
graph classification
(1)
hyperparameter optimization
(1)
kernel learning
(1)
probabilistic inference
(1)
probabilistic modeling
(1)
continual learning
(1)
bayesian inference
(1)
time series forecasting
(1)
variational inference
(1)
Papers
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes
AISTATS 2025
Time-varying Gaussian Process Bandits with Unknown Prior
AISTATS 2025
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
NeurIPS 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Principled Bayesian Optimization in Collaboration with Human Experts
NeurIPS 2024
Bayesian Optimisation of Functions on Graphs
NeurIPS 2023
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
NeurIPS 2022
Bezier Gaussian Processes for Tall and Wide Data
NeurIPS 2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
NeurIPS 2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
NeurIPS 2022
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
NeurIPS 2021
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
NeurIPS 2021
Bayesian Optimization for Probabilistic Programs
NeurIPS 2016
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
NeurIPS 2015
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
NeurIPS 2014