research
∙
04/26/2023
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Recent advances in immunomics have shown that T-cell receptor (TCR) sign...
share
research
∙
12/02/2021
Invariant Priors for Bayesian Quadrature
Bayesian quadrature (BQ) is a model-based numerical integration method t...
research
∙
10/26/2021
Dynamic Causal Bayesian Optimization
This paper studies the problem of performing a sequence of optimal inter...
research
∙
10/25/2021
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in rea...
research
∙
10/18/2021
RKHS-SHAP: Shapley Values for Kernel Methods
Feature attribution for kernel methods is often heuristic and not indivi...
research
∙
06/07/2021
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learnin...
research
∙
02/05/2021
GIBBON: General-purpose Information-Based Bayesian OptimisatioN
This paper describes a general-purpose extension of max-value entropy se...
research
∙
12/31/2020
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has ...
research
∙
10/02/2020
BOSS: Bayesian Optimization over String Spaces
This article develops a Bayesian optimization (BO) method which acts dir...
research
∙
09/27/2020
Multi-task Causal Learning with Gaussian Processes
This paper studies the problem of learning the correlation structure of ...
research
∙
07/18/2020
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer kno...
research
∙
06/06/2020
Learning Inconsistent Preferences with Kernel Methods
We propose a probabilistic kernel approach for preferential learning fro...
research
∙
03/25/2020
Preferential Batch Bayesian Optimization
Most research in Bayesian optimization (BO) has focused on direct feedba...
research
∙
01/28/2020
Bandit optimisation of functions in the Matérn kernel RKHS
We consider the problem of optimising functions in the Reproducing kerne...
research
∙
09/10/2019
Efficient nonmyopic Bayesian optimization and quadrature
Finite-horizon sequential decision problems arise naturally in many mach...
research
∙
05/30/2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
research
∙
05/26/2019
Automatic Discovery of Privacy-Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving ...
research
∙
03/27/2019
Active Multi-Information Source Bayesian Quadrature
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical m...
research
∙
03/18/2019
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but p...
research
∙
04/12/2017
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
research
∙
04/04/2017
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization () is a global optimization strategy designed to f...
research
∙
11/19/2015
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting...
research
∙
10/21/2015
GLASSES: Relieving The Myopia Of Bayesian Optimisation
We present GLASSES: Global optimisation with Look-Ahead through Stochast...
research
∙
05/29/2015
Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
research
∙
05/07/2015
Bayesian Optimization for Synthetic Gene Design
We address the problem of synthetic gene design using Bayesian optimizat...
research
∙
11/14/2013
