19 citations · 35 across the 7 of their papers we have counts for
7 papers
Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimization
Tailin Zhou, Zhilin Chen, Wenlong Lyu +3
Optimizing complex systems, from discovering therapeutic drugs to designing high-performance materials, remains a fundamental challenge across science and engineering, as the under…
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…
Exploring Effective Mask Sampling Modeling for Neural Image Compression
Lin Liu, Mingming Zhao, Shanxin Yuan +5
Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to…
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…
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…
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov +8
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedastic…