14 citations · 97 across the 12 of their papers we have counts for
13 papers
Bayesian Topic Regression for Causal Inference
Maximilian Ahrens, Julian Ashwin, Jan-Peter Calliess +1
Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both…
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
Jack Parker-Holder, Vu Nguyen, Shaan Desai +1
Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters. As such, there has recently been interest in the field…
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
Xingchen Wan, Vu Nguyen, Huong Ha +3
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domain…
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Cheng Li, Sunil Gupta, Santu Rana +3
Scientific experiments are usually expensive due to complex experimental preparation and processing. Experimental design is therefore involved with the task of finding the optimal…
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
Jack Parker-Holder, Vu Nguyen, Stephen Roberts
Many of the recent triumphs in machine learning are dependent on well-tuned hyperparameters. This is particularly prominent in reinforcement learning (RL) where a small change in t…
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning
Samuel Kessler, Vu Nguyen, Stefan Zohren +1
We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically.…
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Binxin Ru, Ahsan S. Alvi, Vu Nguyen +2
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continu…
Knowing The What But Not The Where in Bayesian Optimization
Vu Nguyen, Michael A. Osborne
Bayesian optimization has demonstrated impressive success in finding the optimum input x* and output f* = f(x*) = max f(x) of a black-box function f. In some applications, however,…
Practical Batch Bayesian Optimization for Less Expensive Functions
Vu Nguyen, Sunil Gupta, Santu Rana +2
Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for opti…
High Dimensional Bayesian Optimization Using Dropout
Cheng Li, Sunil Gupta, Santu Rana +3
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Exis…
Budgeted Batch Bayesian Optimization With Unknown Batch Sizes
Vu Nguyen, Santu Rana, Sunil Gupta +2
Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially e…
Scalable Semi-supervised Learning with Graph-based Kernel Machine
Trung Le, Khanh Nguyen, Van Nguyen +2
Acquiring labels are often costly, whereas unlabeled data are usually easy to obtain in modern machine learning applications. Semi-supervised learning provides a principled machine…
Approximation Vector Machines for Large-scale Online Learning
Trung Le, Tu Dinh Nguyen, Vu Nguyen +1
One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity. Sparse models not only improve computation and memory u…