25 citations · 68 across the 10 of their papers we have counts for
11 papers
Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks
Huong Ha, Zongwen Fan, Hongyu Zhang
Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent poten…
An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems
Huong Ha
Monitoring machine learning systems post deployment is critical to ensure the reliability of the systems. Particularly importance is the problem of monitoring the performance of ma…
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Huong Ha, Sunil Gupta, Santu Rana +1
Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding…
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…
High Dimensional Level Set Estimation with Bayesian Neural Network
Huong Ha, Sunil Gupta, Santu Rana +1
Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques…
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Hung Tran-The, Sunil Gupta, Santu Rana +2
Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many pr…
Distributionally Robust Bayesian Quadrature Optimization
Thanh Tang Nguyen, Sunil Gupta, Huong Ha +2
Bayesian quadrature optimization (BQO) maximizes the expectation of an expensive black-box integrand taken over a known probability distribution. In this work, we study BQO under d…
Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta +3
Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Ba…
Useful redundancy in parameter and time delay estimation for continuous-time models
Huong Ha, James S. Welsh, Mazen Alamir
This paper proposes an algorithm to estimate the parameters, including time delay, of continuous time systems based on instrumental variable identification methods. To overcome the…
An analysis of the SPARSEVA estimate for the finite sample data case
Huong Ha, James S. Welsh, Cristian R. Rojas +1
In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is…
Reweighted nuclear norm regularization: A SPARSEVA approach
Huong Ha, James S. Welsh, Niclas Blomberg +2
The aim of this paper is to develop a method to estimate high order FIR and ARX models using least squares with re-weighted nuclear norm regularization. Typically, the choice of th…