Bayesian Optimization

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Self-improving reasoners taking advantage of long-context capabilities (ICLR 2025)
Xingchen Wan, Han Zhou, Ruoxi Sun, Hootan Nakhost, Ke Jiang, Sercan Ö. Arık
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From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Bayesian Optimization of Functions over Node Subsets in Graphs
Neural Information Processing Systems (NeurIPS), 2024
Huidong Liang, Xingchen Wan, Xiaowen Dong
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Bayesian Optimization of Functions over Node Subsets in Graphs
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
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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
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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
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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
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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
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Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
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
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Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
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
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Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels