11 citations · 13 across the 6 of their papers we have counts for
5 papers · 1 filter
Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs
Lin Yang, Junlong Lyu, Wenlong Lyu +1
Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to t…
Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets
Wenlong Lyu, Shoubo Hu, Jie Chuai +1
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. With the increasi…
Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization
Junlong Lyu, Zhitang Chen, Wenlong Lyu +1
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems. Our method investigates the intrinsic connection between posterior distribut…
Universality of parametric Coupling Flows over parametric diffeomorphisms
Junlong Lyu, Zhitang Chen, Chang Feng +5
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression. The approximation universality for CFlows is of p…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…