| High Dimensional Bayesian Optimization Using Dropout C Li, S Gupta, S Rana, V Nugyen, S Venkatesh, A Shilton International Joint Conference on Artificial Intelligence (IJCAI), 2017 | 127 | 2017 |
| High Dimensional Bayesian optimization with Elastic Gaussian process S Rana, C Li, S Gupta, V Nguyen, S Venkatesh International Conference Machine Learning (ICML), 2017 | 125 | 2017 |
| Rapid Bayesian Optimisation for Synthesis of Short Polymer Fiber Materials C Li, D Leal Rubin De Celis, S Rana, S Gupta, A Sutti, S Greenhill, ... scientific report, 2017 | 123 | 2017 |
| Regret for expected improvement over the best-observed value and stopping condition V Nguyen, S Gupta, S Rana, C Li, S Venkatesh Asian conference on machine learning, 279-294, 2017 | 95 | 2017 |
| Hierarchically fair federated learning J Zhang, C Li, A Robles-Kelly, M Kankanhalli arXiv preprint arXiv:2004.10386, 2020 | 79 | 2020 |
| Budgeted batch Bayesian optimization V Nguyen, S Rana, SK Gupta, C Li, S Venkatesh 2016 IEEE 16th International Conference on Data Mining (ICDM), 1107-1112, 2016 | 47 | 2016 |
| Incorporating expert prior in Bayesian optimisation via space warping A Ramachandran, S Gupta, S Rana, C Li, S Venkatesh Knowledge-Based Systems 195, 105663, 2020 | 36 | 2020 |
| Filtering Bayesian optimization approach in weakly specified search space V Nguyen, S Gupta, S Rana, C Li, S Venkatesh Knowledge and Information Systems 60, 385-413, 2019 | 32 | 2019 |
| Accelerating Experimental Design by Incorporating Experimenter Hunches C Li, S Rana, S Gupta, V Nguyen, S Venkatesh, A Sutti, D Rubin, T Slezak, ... IEEE International Conference on Data Mining (ICDM), 2018 | 30 | 2018 |
| Bayesian optimization in weakly specified search space V Nguyen, S Gupta, S Rane, C Li, S Venkatesh 2017 IEEE International Conference on data mining (ICDM), 347-356, 2017 | 24 | 2017 |
| Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records C Li, S Rana, D Phung, S Venkatesh Knowledge-Based Systems 99, 168-182, 2016 | 21 | 2016 |
| A Bayesian nonparametric approach for multi-label classification V Nguyen, S Gupta, S Rana, C Li, S Venkatesh Asian conference on machine learning, 254-269, 2016 | 19 | 2016 |
| Explaining Black-box Machine Learning models via Interpretable Surrogates D Kuttichira, S Gupta, C Li, S Rana, S Venkatesh Pacific Rim International Conference on Artificial Intelligence, 2019 | 12* | 2019 |
| Optimizing a high-entropy system: software-assisted development of highly hydrophobic surfaces using an amphiphilic polymer S Subianto, C Li, D Rubin de Celis Leal, S Rana, S Gupta, R He, ... ACS omega 4 (14), 15912-15922, 2019 | 11 | 2019 |
| Budgeted batch Bayesian optimization with unknown batch sizes V Nguyen, S Rana, S Gupta, C Li, S Venkatesh arXiv preprint arXiv:1703.04842, 2017 | 10 | 2017 |
| Bayesian Optimization with Monotonicity Information C Li, S Rana, S Gupta, N Vu, V Svetha. NIPS Workshop on Bayesian Optimization 2017, 2017 | 9 | 2017 |
| Data clustering using side information dependent Chinese restaurant processes C Li, S Rana, D Phung, S Venkatesh Knowledge and information systems 47, 463-488, 2016 | 9 | 2016 |
| Exploiting side information in distance dependent chinese restaurant processes for data clustering C Li, D Phung, S Rana, S Venkatesh 2013 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2013 | 8 | 2013 |
| Incorporating expert prior knowledge into experimental design via posterior sampling C Li, S Gupta, S Rana, V Nguyen, A Robles-Kelly, S Venkatesh arXiv preprint arXiv:2002.11256, 2020 | 7 | 2020 |
| Sparse Approximation for Gaussian Process with Derivative Observations A Yang, C Li, S Rana, S Gupta, V Svetha Australasian Joint Conference on Artificial Intelligence, 2018 | 6 | 2018 |
Svetha VenkateshDeakin Distinguished Professor, Deakin UniversityVerified email at deakin.edu.au
