2024

L. Pham, H. Ha, H. Zhang. Root Cause Analysis for Microservices based on Causal Inference: How Far Are We? In the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2024. (CORE A*)

K. Taylor, H. Ha, M. Li, X. Li, J. Chan. Accelerated Bayesian Preference Learning for Efficient Evolutionary Multi-objective Optimisation. In the Genetic and Evolutionary Computation Conference (GECCO) (short paper), 2024. (CORE A)

L. Pham, H. Ha, H. Zhang. BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection, 2023. In the ACM International Conference on the Foundations of Software Engineering (FSE), 2024. (CORE A*)

L. Ngo, H. Ha, J. Chan, V. Nguyen, H. Zhang. High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy. In Transaction of Machine Learning Research (TMLR), 2024.

2022

H. Ha. An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems. In the ML Safety Workshop at NeurIPS, 2022.

2021

X. Wan, V. Nguyen, H. Ha, B. Ru, C. Lu, M.A. Osborne. Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. In the 38th International Conference on Machine Learning (ICML), 2021. (CORE A*)

K. Taylor, H. Ha, M. Li, J. Chan, X. Li. Bayesian Preference Learning for Interactive Multi-objective Optimisation. In the Genetic and Evolutionary Computation Conference (GECCO), 2021. (CORE A)

H. Ha, S. Gupta, S. Rana, S. Venkatesh. ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms. In the RobustML workshop at ICLR, 2021.

H. Ha, S. Gupta, S. Rana, S. Venkatesh. High Dimensional Level Set Estimation with Bayesian Neural Network. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. (CORE A*)

2020

H. Tran-The, S. Gupta, S. Rana, H. Ha, S. Venkatesh. Efficient Sub-linear Regret Algorithms for Bayesian Optimisation with Unknown Search Bounds. In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. (CORE A*)

T.T. Nguyen, S. Gupta, H. Ha, S. Rana, S. Venkatesh. Distributionally Robust Bayesian Quadrature Optimization. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. (CORE A)

2019

H. Ha, S. Rana, S. Gupta, T. Nguyen, H. Tran-The, S. Venkatesh. Bayesian Optimization with Unknown Search Space. In Advances in Neural Information Processing Systems 32 (NeurIPS), 2019. (CORE A*)

H. Ha, H. Zhang. Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression. In Proceedings of the 35th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2019. (CORE A)

H. Ha, H. Zhang. DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network. In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE), 2019. (CORE A*)

2018

H. Ha, J.S. Welsh, C.R. Rojas, and B. Wahlberg. An analysis of the SPARSEVA estimate for the finite sampled data case. In Automatica 96, pages 141-149, 2018. (JCR-Q1)

H. Ha, J.S. Welsh and M. Alamir. Useful redundancy in parameter and time delay estimation for continuous-time models. In Automatica 95, pages 455-462, 2018. (JCR-Q1)

2016

H. Ha and J.S. Welsh. Parameter and delay estimation of continuous-time models utilizing multiple filtering. In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), 2016. (CORE A)

2015

H. Ha and J.S. Welsh. Model Order Selection for Continuous Time Instrumental Variable Methods Using Regularization. In Proceedings of the 54th IEEE Conference on Decision and Control (CDC), 2015. (CORE A)

H. Ha, J.S. Welsh, N. Blomberg, C. R. Rojas, and B. Wahlberg. Reweighted nuclear norm regularization: A SPARSEVA approach. In Proceedings of the 17th IFAC Symposium on System Identification (SYSID), 2015.

2014

H. Ha and J.S. Welsh. Ensuring stability in continuous time system identification instrumental variable method for over-parameterized models. In Proceedings of the 53rd IEEE Conference on Decision and Control (CDC), 2014. (CORE A)