Bayesian Optimization

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach

International Conference on Artificial Intelligence and Statistics (AISTATS)

Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael Osborne

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach

Bayesian Optimisation of Functions on Graphs

Neural Information Processing Systems (NeurIPS), 2023

Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael Osborne, Xiaowen Dong

Bayesian Optimisation of Functions on Graphs

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Neural Information Processing Systems (NeurIPS), 2022

Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael Osborne, Eytan Bakshy

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

BOiLS: Bayesian Optimisation for Logic Synthesis

Design, Automation and Test in Europe (DATE), 2022

Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou Ammar

BOiLS: Bayesian Optimisation for Logic Synthesis

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation

Neural Information Processing Systems (NeurIPS), 2021

Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael Osborne, Xiaowen Dong

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces

State-of-the-art Bayesian optimization in high-dimensional and non-continuous spaces (ICML 2021)

Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael Osborne

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

Performant and efficient NAS method combining BO and graph kernels (ICLR 2021)

Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne

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