AutoML

Bayesian Quadrature for Neural Ensemble Search
Transactions on Machine Learning Research (TMLR)
Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael Osborne
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Bayesian Quadrature for Neural Ensemble Search
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
Bayesian Generational Population-Based Training
International Conference on Automated Machine Learning (AutoML-Conf), 2022
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne
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Bayesian Generational Population-Based Training
On Redundancy and Diversity in Cell-based Neural Architecture Search
International Conference on Learning Representations (ICLR), 2022.
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li
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On Redundancy and Diversity in Cell-based Neural Architecture Search
Approximate Neural Architecture Search via Operation Distribution Learning
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio M. Carlucci
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Approximate Neural Architecture Search via Operation Distribution Learning
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