research
∙
03/03/2023
Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
Bayesian Optimization (BO) is an effective approach for global optimizat...
share
research
∙
03/15/2022
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
The expected improvement (EI) algorithm is one of the most popular strat...
research
∙
07/24/2021
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
We address policy learning with logged data in contextual bandits. Curre...
research
∙
05/10/2021
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for findi...
research
∙
03/11/2021
On Finite-Sample Analysis of Offline Reinforcement Learning with Deep ReLU Networks
This paper studies the statistical theory of offline reinforcement learn...
research
∙
09/05/2020
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of ...
research
∙
11/27/2019
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is ...
research
∙
10/29/2019
