no code implementations • 9 Aug 2023 • Wenlong Lyu, Shoubo Hu, Jie Chuai, Zhitang Chen
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it relies on a surrogate model to approximate the black-box response function.
no code implementations • 9 Jun 2023 • Lin Liu, Mingming Zhao, Shanxin Yuan, Wenlong Lyu, Wengang Zhou, Houqiang Li, Yanfeng Wang, Qi Tian
Specifically, Cube Mask Sampling Module (CMSM) is proposed to apply both spatial and channel mask sampling modeling to image compression in the pre-training stage.
no code implementations • 30 Jan 2023 • Junlong Lyu, Zhitang Chen, Wenlong Lyu, Jianye Hao
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems.
2 code implementations • 7 Jun 2021 • Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on Molecular Graph Generation on ZINC
3 code implementations • 7 Dec 2020 • Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, Jun Wang, Jan Peters, Haitham Bou Ammar
Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
Ranked #1 on Hyperparameter Optimization on Bayesmark
no code implementations • 1 Dec 2019 • Shuhan Zhang, Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis.
1 code implementation • ICML 2018 • Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng
In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions.