Publications
M Buckley, A Ndukwe, P Nair, S Rana, K Fairfull-Smith, N Gandhi
(2023), Vol. 12, pp. 1-23, Antibiotics, Basel, Switzerland, C1
K Huckvale, L Hoon, E Stech, J Newby, W Zheng, J Han, R Vasa, S Gupta, S Barnett, M Senadeera, S Cameron, S Kurniawan, A Agarwal, J Kupper, J Asbury, D Willie, A Grant, H Cutler, B Parkinson, A Ahumada-Canale, J Beames, R Logothetis, M Bautista, J Rosenberg, A Shvetcov, T Quinn, A MacKinnon, S Rana, T Tran, S Rosenbaum, K Mouzakis, A Werner-Seidler, A Whitton, S Venkatesh, H Christensen
(2023), Vol. 13, pp. e066249-e066249, BMJ Open, England, C1
Machine learning for predicting the outcome of terminal ballistics events
Shannon Ryan, Neeraj Sushma, Arun Kumar AV, Julian Berk, Tahrima Hashem, Santu Rana, Svetha Venkatesh
(2023), pp. 1-13, Defence Technology, Amsterdam, The Netherlands, C1
S Ryan, N Sushma, H Le, A Arun Kumar, J Berk, T Nguyen, S Rana, S Kandanaarachchi, S Venkatesh
(2023), Vol. 181, International Journal of Impact Engineering, C1
Balanced Q-learning: Combining the influence of optimistic and pessimistic targets
T George Karimpanal, H Le, M Abdolshah, S Rana, S Gupta, T Tran, S Venkatesh
(2023), Vol. 325, pp. 1-21, Artificial Intelligence, Amsterdam, The Netherlands, C1
Controlled Diversity with Preference: Towards Learning a Diverse Set of Desired Skills
M Hussonnois, T Karimpanal, S Rana
(2023), Vol. 2023-May, pp. 1135-1143, AAMAS 2023 : Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, London, England, E1
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
A Shilton, S Gupta, S Rana, S Venkatesh
(2023), Vol. 202, pp. 31435-31488, PMLR 2023 : Proceedings International Conference Machine Learning Research, Honolulu, Hawaii, USA, E1
Prescriptive analytics with differential privacy
H Harikumar, S Rana, S Gupta, T Nguyen, R Kaimal, S Venkatesh
(2022), Vol. 13, pp. 123-138, International Journal of Data Science and Analytics, C1
S Ryan, J Berk, S Rana, B McDonald, S Venkatesh
(2022), Vol. 18, pp. 1563-1577, Defence Technology, Amsterdam, The Netherlands, C1
Verification of integrity of deployed deep learning models using Bayesian Optimization
D Kuttichira, S Gupta, D Nguyen, S Rana, S Venkatesh
(2022), Vol. 241, pp. 1-12, Knowledge-Based Systems, Amsterdam, The Netherlands, C1
Dual-frame spatio-temporal feature modulation for video enhancement
P Patil, S Gupta, S Rana, S Venkatesh
(2022), Vol. 130, pp. 1-14, Pattern Recognition, Amsterdam, The Netherlands, C1
K Deane, Y Yang, J Licavoli, V Nguyen, S Rana, S Gupta, S Venkatesh, P Sanders
(2022), Vol. 12, pp. 1-19, Metals, Basel, Switzerland, C1
Shannon Ryan, Julian Berk, Santu Rana, Brodie McDonald, Svetha Venkatesh
(2022), Vol. 18, pp. 1563-1577, DEFENCE TECHNOLOGY, C1
Real-Time Skill Discovery in Intelligent Virtual Assistants
P Gopal, S Gupta, S Rana, V Le, T Nguyen, S Venkatesh
(2022), Vol. 13280, pp. 315-327, PAKDD 2022 : Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022, Chengdu, China, E1
Sympathy-based Reinforcement Learning Agents
M Senadeera, T Karimpanal, S Gupta, S Rana
(2022), Vol. 2, pp. 1164-1172, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, E1
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
H Tran-The, S Gupta, S Rana, S Venkatesh
(2022), Vol. 151, pp. 8715-8737, AISTATS 2022 : Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual Conference, E1
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
K Do, H Harikumar, H Le, D Nguyen, T Tran, S Rana, D Nguyen, W Susilo, S Venkatesh
(2022), Vol. 13665 LNCS, pp. 283-300, ECCV 2022 : Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, E1
Video Restoration Framework and Its Meta-adaptations to Data-Poor Conditions
P Patil, S Gupta, S Rana, S Venkatesh
(2022), Vol. 13688, pp. 143-160, ECCV 2022 : Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, E1
Fast Model-based Policy Search for Universal Policy Networks
B Semage, T George Karimpanal, S Rana, S Venkatesh
(2022), Vol. 2022-August, pp. 2314-2320, ICPR 2022 : Proceedings of the 26th International Conference on Pattern Recognition, Montreal, Quebec, E1
Uncertainty Aware System Identification with Universal Policies
B Semage, T George Karimpanal, S Rana, S Venkatesh
(2022), Vol. 2022-August, pp. 2321-2327, ICPR 2022 : Proceedings of the 26th International Conference on Pattern Recognition, Montreal, Quebec, E1
K Do, H Le, D Nguyen, D Nguyen, H Harikumar, T Tran, S Rana, S Venkatesh
(2022), Vol. 35, pp. 1-19, NeurIPS 2022 : Proceedings of the 36th Neural Information Processing Systems Conference 2022, New Orleans, La., E1
Human-AI Collaborative Bayesian Optimisation
A Arun Kumar, S Rana, A Shilton, S Venkatesh
(2022), Vol. 35, pp. 1-13, NeurIPS 2022 : Proceedings of the 2022 Neural Information Processing Systems Conference, Virtual Conference, E1
Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing
K Ruberu, M Senadeera, S Rana, S Gupta, J Chung, Z Yue, S Venkatesh, G Wallace
(2021), Vol. 22, Applied Materials Today, C1
Predictive Model Based on Health Data Analysis for Risk of Readmission in Disease-Specific Cohorts
M Ansari, A Alok, D Jain, S Rana, S Gupta, R Salwan, S Venkatesh
(2021), Vol. 18, pp. 1-11, Perspectives in health information management, Chicago, Ill., C1
Fairness improvement for black-box classifiers with Gaussian process
D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh
(2021), Vol. 576, pp. 542-556, Information Sciences, C1
H Harikumar, T Quinn, S Rana, S Gupta, S Venkatesh
(2021), Vol. 14, BioData Mining, England, C1
J Joseph, M Senadeera, Q Chao, K Shamlaye, S Rana, S Gupta, S Venkatesh, P Hodgson, M Barnett, D Fabijanic
(2021), Vol. 888, Journal of Alloys and Compounds, C1
Identification of predictors and model for predicting prolonged length of stay in dengue patients
M Shahid Ansari, D Jain, H Harikumar, S Rana, S Gupta, S Budhiraja, S Venkatesh
(2021), Vol. 24, pp. 786-798, Health Care Management Science, Netherlands, C1
Adaptive cost-aware Bayesian optimization[Formula presented]
P Luong, D Nguyen, S Gupta, S Rana, S Venkatesh
(2021), Vol. 232, Knowledge-Based Systems, C1
S Rana, W Luo, T Tran, S Venkatesh, P Talman, T Phan, D Phung, B Clissold
(2021), Vol. 12, Frontiers in Neurology, Switzerland, C1
Bayesian Optimization with Missing Inputs
P Luong, D Nguyen, S Gupta, S Rana, S Venkatesh
(2021), Vol. 12458, pp. 691-706, ECML PKDD 2020 : Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Belgium, Ghent, E1
Scalable Backdoor Detection in Neural Networks
H Harikumar, V Le, S Rana, S Bhattacharya, S Gupta, S Venkatesh
(2021), Vol. 12458, pp. 289-304, ECML PKDD 2020 : Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Belgium, Ghent, E1
Sparse Spectrum Gaussian Process for Bayesian Optimization
A Yang, C Li, S Rana, S Gupta, S Venkatesh
(2021), Vol. 12713, pp. 257-268, PAKDD 2021 : Advances in Knowledge Discovery and Data Mining : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I, Virtual Event, E1
Factor screening using Bayesian active learning and gaussian process meta-modelling
C Li, D Nguyen, S Rana, S Gupta, A Gill, S Venkatesh
(2021), pp. 3288-3295, ICPR 2020 : Proceedings of the 25th International Conference on Pattern Recognition, Online from Milan, Italy, E1
High Dimensional Level Set Estimation with Bayesian Neural Network
Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2021), Vol. 35, pp. 12095-12103, AAAI 2021 : Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Event, E1
A New Representation of Successor Features for Transfer across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen George, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2021), Vol. 139, pp. 1-14, ICML 2021 : Proceedings of the International Conference of Machine Learning, Virtual Conference, E1
Knowledge Distillation with Distribution Mismatch
D Nguyen, S Gupta, T Nguyen, S Rana, P Nguyen, T Tran, K Le, S Ryan, S Venkatesh
(2021), Vol. 12976, pp. 250-265, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Fast Conditional Network Compression Using Bayesian HyperNetworks
P Nguyen, T Tran, K Le, S Gupta, S Rana, D Nguyen, T Nguyen, S Ryan, S Venkatesh
(2021), Vol. 12977, pp. 330-345, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Variational Hyper-encoding Networks
P Nguyen, T Tran, S Gupta, S Rana, H Dam, S Venkatesh
(2021), Vol. 12976, pp. 100-115, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Targeted Universal Adversarial Perturbations for Automatic Speech Recognition
W Zong, Y Chow, W Susilo, S Rana, S Venkatesh
(2021), Vol. 13118, pp. 358-373, ISC 2021 : Proceedings of the 24th Information Security International Conference, Virtual Event, E1
Kernel Functional Optimisation
A Arun Kumar, A Shilton, S Rana, S Gupta, S Venkatesh
(2021), Vol. 6, pp. 4725-4737, NeurIPS 2021 : Proceedings of the 35th Conference on Neural Information Processing Systems, Virtual Conference, E1
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
H Tran-The, S Gupta, S Rana, S Venkatesh
(2021), Vol. 139, pp. 10390-10400, Proceedings of Machine Learning Research, E1
Batch Bayesian optimization using multi-scale search
T Joy, S Rana, S Gupta, S Venkatesh
(2020), Vol. 187, Knowledge-Based Systems, C1
S Allender, J Hayward, S Gupta, A Sanigorski, S Rana, H Seward, S Jacobs, S Venkatesh
(2020), Vol. 3, npj Digital Medicine, England, C1
Incorporating expert prior in Bayesian optimisation via space warping
A Ramachandran, S Gupta, S Rana, C Li, S Venkatesh
(2020), Vol. 195, Knowledge-Based Systems, C1
Bayesian optimisation in unknown bounded search domains
J Berk, S Gupta, S Rana, V Nguyen, S Venkatesh
(2020), Vol. 195, Knowledge-Based Systems, C1
Bayesian optimization for adaptive experimental design: a review
S Greenhill, S Rana, S Gupta, P Vellanki, S Venkatesh
(2020), Vol. 8, pp. 13937-13948, IEEE access, Piscataway, N.J., C1
Improving the tensile properties of wet spun silk fibers using rapid Bayesian algorithm
Ya Yao, Benjamin Allardyce, Rangam Rajkhowa, Dylan Hegh, Alessandra Sutti, Surya Subianto, Sunil Gupta, Santu Rana, S Greenhill, Svetha Venkatesh, Xungai Wang, Joselito Razal
(2020), Vol. 6, pp. 3197-3207, ACS biomaterials science and engineering, Washington, D.C., C1
Fast hyperparameter tuning using Bayesian optimization with directional derivatives
T Joy, S Rana, S Gupta, S Venkatesh
(2020), Vol. 205, Knowledge-Based Systems, C1
A scrap-tolerant alloying concept based on high entropy alloys
M Barnett, M Senadeera, D Fabijanic, K Shamlaye, J Joseph, S Kada, S Rana, S Gupta, S Venkatesh
(2020), Vol. 200, pp. 735-744, Acta Materialia, Amsterdam, The Netherlands, C1
Level set estimation with search space warping
Manisha Senadeera, S Rana, S Gupta, S Venkatesh
(2020), Vol. 12085, pp. 827-839, PAKDD 2020 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 24th Pacific-Asia Conference 2020, Singapore, E1
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2020), pp. 2284-2290, IJCAI-PRICAI-20 : Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, E1
Distributionally Robust Bayesian Quadrature Optimization
Tang Thanh, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh
(2020), Vol. 108, pp. 1921-1930, AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Online from Palermo, Italy, E1
Accelerated Bayesian Optimization through Weight-Prior Tuning
Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Thomas Dorin, Alessandra Sutti, David Rubin, Teo Slezak, Alireza Vahid, Murray Height
(2020), Vol. 108, pp. 1-10, AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Online from Palermo, Italy, E1
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
T Karimpanal, S Rana, S Gupta, T Tran, S Venkatesh
(2020), pp. 1-10, IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Scotland, E1
Unsupervised Anomaly Detection on Temporal Multiway Data
D Nguyen, P Nguyen, K Do, S Rana, S Gupta, T Tran
(2020), pp. 1059-1066, SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, Canberra, Australian Capital Territory, E1
DeepCoDA: Personalized interpretability for compositional health data
T Quinn, D Nguyen, S Rana, S Gupta, S Venkatesh
(2020), Vol. PartF168147-11, pp. 7833-7842, ICML 2020 : Proceedings of the 37th International Conference on Machine Learning, Online, E1
Trading convergence rate with computational budget in high dimensional Bayesian optimization
H Tran The, S Gupta, S Rana, S Venkatesh
(2020), pp. 2425-2432, AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, New York, N.Y., E1
Bayesian optimization for categorical and category-specific continuous inputs
D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh
(2020), pp. 5256-5263, AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, New York, N.Y., E1
Filtering Bayesian optimization approach in weakly specified search space
V Nguyen, S Gupta, S Rana, C Li, S Venkatesh
(2019), Vol. 60, pp. 385-413, Knowledge and Information Systems, C1
A flexible transfer learning framework for Bayesian optimization with convergence guarantee
T Theckel Joy, S Rana, S Gupta, S Venkatesh
(2019), Vol. 115, pp. 656-672, Expert Systems with Applications, C1
S Subianto, C Li, D Rubin De Celis Leal, S Rana, S Gupta, R He, S Venkatesh, A Sutti
(2019), Vol. 4, pp. 15912-15922, ACS Omega, United States, C1
Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes
D Rubín De Celis Leal, D Nguyen, P Vellanki, C Li, S Rana, N Thompson, S Gupta, K Pringle, S Subianto, S Venkatesh, T Slezak, M Height, A Sutti
(2019), Vol. 4, pp. 20571-20578, ACS Omega, United States, C1
Information-theoretic transfer learning framework for Bayesian optimisation
A Ramachandran, S Gupta, S Rana, S Venkatesh
(2019), Vol. 11052, pp. 827-842, ECML-PKDD 2018 : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018, Dublin, Ireland, E1
Exploration enhanced expected improvement for Bayesian optimization
J Berk, V Nguyen, S Gupta, S Rana, S Venkatesh
(2019), Vol. 11052, pp. 621-637, ECML-PKDD 2018 : Proceedings of the e European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018, Dublin, Ireland, E1
Incomplete conditional density estimation for fast materials discovery
P Nguyen, T Tran, S Gupta, S Rana, M Barnett, S Venkatesh
(2019), pp. 549-557, 2019 SIAM : Proceedings of the 2019 SIAM International Conference on Data Mining, Calgary, Alta., E1
Bayesian functional optimisation with shape prior
Pratibha Vellanki, Santu Rana, Sunil Gupta, David Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh
(2019), pp. 1617-1624, AAAI 2019 : Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, E1
Explaining black-box models using interpretable surrogates
D Kuttichira, S Gupta, C Li, S Rana, S Venkatesh
(2019), Vol. 11670, pp. 3-15, PRICAI 2019: Trends in Artificial Intelligence 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings,, Cuvu, Fiji, E1
Bayesian Optimisation for Objective Functions with Varying Smoothness
A Arun Kumar, S Rana, C Li, S Gupta, A Shilton, S Venkatesh
(2019), Vol. 11919 LNAI, pp. 460-472, AI 2019: Advances in Artificial Intelligence 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2–5, 2019, Proceedings, Adelaide, South Australia, E1
Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation
A Ramachandran, S Gupta, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 497-509, AI 2019: Advances in artificial intelligence : Proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence 2019, Adelaide, South Australia, E1
Bayesian Optimization with Discrete Variables
P Luong, S Gupta, D Nguyen, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 473-484, AI 2019 : Advances in Artificial Intelligence : Proceedings of the 32nd Australian Joint Conference, Adelaide, South Australia, E1
Detection of Compromised Models Using Bayesian Optimization
D Kuttichira, S Gupta, D Nguyen, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 485-496, AI 2019 : Advances in Artificial Intelligence : Proceedings of the 32nd Australian Joint Conference, Adelaide, South Australia, E1
Efficient bayesian optimization for uncertainty reduction over perceived optima locations
V Nguyen, S Gupta, S Rana, M Thai, C Li, S Venkatesh
(2019), Vol. 2019-November, pp. 1270-1275, ICDM 2019 : Proceedings of the 19th IEEE International Conference on Data Mining, Beijing, China, E1
Bayesian optimization with unknown search space
H Ha, S Rana, S Gupta, T Nguyen, H Tran-The, S Venkatesh
(2019), Vol. 32, pp. 1-10, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
Multi-objective Bayesian optimisation with preferences over objectives
M Abdolshah, A Shilton, S Rana, S Gupta, S Venkatesh
(2019), Vol. 32, pp. 1-11, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
Sub-linear regret bounds for Bayesian optimisation in unknown search spaces
H Tran-The, S Gupta, S Rana, H Ha, S Venkatesh
(2019), Vol. 2020-December, pp. 1-25, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
New bayesian-optimization-based design of high-strength 7xxx-series alloys from recycled aluminum
A Vahid, S Rana, S Gupta, P Vellanki, S Venkatesh, T Dorin
(2018), Vol. 70, pp. 2704-2709, JOM, New York, N.Y., C1
Exploiting strategy-space diversity for batch Bayesian optimisation
S Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh
(2018), Vol. 84, pp. 538-547, AISTATS 2018 : Proceedings of the International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, Canary Islands, E1
A privacy preserving bayesian optimization with high efficiency
T Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2018), Vol. 10939, pp. 543-555, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1
Prescriptive analytics through constrained bayesian optimization
Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, Kaimal Ramachandra, Svetha Venkatesh
(2018), Vol. 10937, pp. 335-347, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1
Efficient Bayesian optimisation using derivative meta-model
A Yang, C Li, S Rana, S Gupta, S Venkatesh
(2018), Vol. 11013, pp. 256-264, PRICAI 2018: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, E1
Selecting optimal source for transfer learning in Bayesian optimisation
A Ramachandran, S Gupta, S Rana, S Venkatesh
(2018), Vol. 11012, pp. 42-56, PRICAI 2018 : Trends in artificial intelligence : Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, E1
Multi-target optimisation via Bayesian optimisation and linear programming
A Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
(2018), UAI 2018: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, Monterey, Calif., E1
Expected hypervolume improvement with constraints
M Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, svetha Venkatesh
(2018), pp. 3238-3243, ICPR 2018 : Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1
Sparse approximation for Gaussian process with derivative observations
A Yang, C Li, S Rana, S Gupta, S Venkatesh
(2018), Vol. 11320, pp. 507-518, AI 2018: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, Wellington, N.Z., E1
Accelerating experimental design by incorporating experimenter hunches
C Li, R Santu, S Gupta, V Nguyen, S Venkatesh, A Sutti, D De Celis Leal, T Slezak, M Height, M Mohammed, I Gibson
(2018), Vol. 2018-November, pp. 257-266, IEEE ICDM 2018 : International Conference on Data Mining, Singapore, E1
Differentially private prescriptive analytics
H Harikumar, S Rana, S Gupta, T Nguyen, R Kaimal, S Venkatesh
(2018), Vol. 2018-November, pp. 995-1000, ICDM 2018 : Proceedings of the IEEE International Conference on Data Mining, Singapore, E1
Algorithmic assurance: an active approach to algorithmic testing using Bayesian optimisation
Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Nguyen Vu, Svetha Venkatesh
(2018), Vol. 31, pp. 1-9, NeurIPS 2018 : Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, Canada, E1
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
C Li, D Rubín De Celis Leal, S Rana, S Gupta, A Sutti, S Greenhill, T Slezak, M Height, S Venkatesh
(2017), Vol. 7, Scientific Reports, England, C1
Regret bounds for transfer learning in Bayesian optimisation
A Shilton, S Gupta, S Rana, S Venkatesh
(2017), Vol. 54, pp. 1-9, AISTATS 2017 : Machine Learning Research : Proceedings of the 20th Artificial Intelligence and Statistics International Conference, Fort Lauderdale, Florida, E1
T Nguyen, S Gupta, S Rana, S Venkatesh
(2017), Vol. 54, pp. 578-591, PAKDD 2017 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 21st Pacific-Asia Conference, Jeju, South Korea, E1
High dimensional bayesian optimization with elastic gaussian process
S Rana, C Li, S Gupta, V Nguyen, S Venkatesh
(2017), pp. 1-9, ICML 2017 Proceedings of the International Conference in Machine Learning, Sydney, New South Wales, E1
High dimensional bayesian optimization using dropout
C Li, S gupta, S Rana, V Nguyen, S Venkatesh, A Shilton
(2017), pp. 2096-2102, IJCAI 2017 : Proceedings of the 26th International Joint Confrerence on Artificial Intelligence, Melbourne, Victoria, E1
Bayesian optimization in weakly specified search space
V Nguyen, S Gupta, S Rana, C Li, S Venkatesh
(2017), pp. 347-356, ICDM 2017 : Proceedings of the IEEE International Conference on Data Mining, New Orleans, La., E1
Process-constrained batch Bayesian optimisation
P Vellanki, S Rana, S Gupta, D Rubin, A Sutti, T Dorin, M Height, P Sandars, S Venkatesh
(2017), Vol. 2017-December, pp. 3415-3424, NIPS 2017 : Proceedings of the 31st Conference of Neural Information Processing Systems, Long Beach, California, E1
Regret for expected improvement over the best-observed value and stopping condition
V Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh
(2017), pp. 1-16, ACML 2017 : Proceedings of the Ninth Asian Conference on Machine Learning, Seoul, Korea, E1
Flexible transfer learning framework for bayesian optimisation
T Joy, S Rana, S Gupta, S Venkatesh
(2016), Vol. 9651, pp. 102-114, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1
Toxicity prediction in cancer using multiple instance learning in a multi-task framework
C Li, S Gupta, S Rana, W Luo, S Venkatesh, D Ashely, D Phung
(2016), Vol. 9651, pp. 152-164, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), B1
Privacy aware K-means clustering with high utility
T Nguyen, S Gupta, S Rana, S Venkatesh
(2016), Vol. 9652, pp. 388-400, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1
Data clustering using side information dependent Chinese restaurant processes
C Li, S Rana, D Phung, S Venkatesh
(2016), Vol. 47, pp. 463-488, Knowledge and Information Systems, C1
A new transfer learning framework with application to model-agnostic multi-task learning
S Gupta, S Rana, B Saha, D Phung, S Venkatesh
(2016), Vol. 49, pp. 933-973, Knowledge and Information Systems, C1
Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records
C Li, S Rana, D Phung, S Venkatesh
(2016), Vol. 99, pp. 168-182, Knowledge-Based Systems, C1
Dirichlet process mixture models with pairwise constraints for data clustering
C Li, S Rana, Q Phung, S Venkatesh
(2016), Vol. 3, pp. 205-223, Annals of data science, Berlin, Germany, C1
W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk
(2016), Vol. 18, Journal of Medical Internet Research, Canada, C1
Differentially private multi-task learning
S Rana, S Rana, S Gupta, S Gupta, S Venkatesh, S Venkatesh
(2016), Vol. 9650, pp. 101-113, PAISI 2016 : Intelligence and Security Informatics : Proceedings of the 11th Pacific-Asia Workshop, Auckland, New Zealand, E1
Extracting key challenges in achieving sobriety through shared subspace learning
H Harikumar, T Nguyen, S Rana, S Gupta, R Kaimal, S Venkatesh
(2016), Vol. 10086, pp. 420-433, ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Gold Coast, Queensland, E1
H Harikumar, T Nguyen, S Gupta, S Rana, R Kaimal, S Venkatesh
(2016), Vol. 10086, pp. 520-533, ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Gold Coast, Queensland, E1
T Nguyen, S Gupta, S Rana, V Nguyen, S Venkatesh, K Deane, P Sanders
(2016), Vol. 9992, pp. 268-280, AI 2016: Advances in Artificial Intelligence : Proceedings of the Australasian Joint Conference, Hobart, Tasmania, E1
Budgeted batch Bayesian optimization
V Nguyen, S Rana, S Gupta, C Li, S Venkatesh
(2016), pp. 1107-1112, ICDM 2016: Proceedings of the 16th IEEE International Conference on Data Mining, Barcelona, Spain, E1
Hyperparameter tuning for big data using Bayesian optimisation
T Theckel Joy, S Rana, S Gupta, S Venkatesh
(2016), pp. 2574-2579, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1
Multiple adverse effects prediction in longitudinal cancer treatment
C Li, S Gupta, S Rana, T Nguyen, S Venkatesh, D Ashley, P Livingston
(2016), pp. 3156-3161, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1
Bayesian nonparametric Multiple Instance Regression
S Subramanian, S Rana, S Gupta, S Venkatesh, P Sivakumar, C Velayutham
(2016), pp. 3661-3666, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1
A bayesian nonparametric approach for multi-label classification
T Nguyen, S Gupta, S Rana, C Li, S Venkatesh
(2016), Vol. 63, pp. 254-269, ACML 2016: Proceedings of the 8th Asian Conference on Machine Learning, Hamilton, New Zealand, E1
Small-variance asymptotics for bayesian nonparametric models with constraints
C Li, S Rana, Q Phung, S Venkatesh
(2015), Vol. 9078, pp. 92-105, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1
Collaborating differently on different topics: a multi-relational approach to multi-task learning
S Gupta, S Rana, Q Phung, S Venkatesh
(2015), Vol. 9077, pp. 303-316, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1
Web search activity data accurately predict population chronic disease risk in the USA
T Nguyen, T Tran, W Luo, S Gupta, S Rana, Q Phung, M Nichols, L Millar, S Venkatesh, S Allender
(2015), Vol. 69, pp. 693-699, Journal of epidemiology and community health, London, Eng., C1
A predictive framework for modeling healthcare data with evolving clinical interventions
S Rana, S Gupta, Q Phung, S Venkatesh
(2015), Vol. 8, pp. 162-182, Statistical analysis and data mining, London, Eng., C1
W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, Q Phung, S Venkatesh, S Allender
(2015), Vol. 10, pp. 1-13, PLoS One, San Francisco, Calif., C1
Differentially private random forest with high utility
S Rana, S Gupta, S Venkatesh
(2015), pp. 955-960, ICDM 2015: Proceedings of the 15th IEEE International Conference on Data Mining, Atlantic City, New Jersey, E1
S Gupta, S Rana, Q Phung, S Venkatesh
(2015), pp. 703-711, SDM 2015: Proceedings of the 15th SIAM International Conference on Data Mining, Vancouver, British Columbia, E1
Intervention-driven predictive framework for modeling healthcare data
S Rana, S Gupta, D Phung, S Venkatesh
(2014), Vol. 8443 Part 1, pp. 497-508, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany, B1
S Rana, T Tran, W Luo, D Phung, R Kennedy, S Venkatesh
(2014), Vol. 38, pp. 377-382, Australian health review, Melbourne, Vic., C1
A framework for feature extraction from hospital medical data with applications in risk prediction
T Truyen, W Luo, P Dinh, S Gupta, S Rana, R Kennedy, A Larkins, S Venkatesh
(2014), Vol. 15, pp. 1-9, BMC Bioinformatics, London, Eng., C1
Regularizing topic discovery in emrs with side information by using hierarchical bayesian models
C Li, S Rana, D Phung, S Venkatesh
(2014), pp. 1307-1312, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1
S Gupta, S Rana, Q Phung, S Venkatesh
(2014), pp. 235-243, SDM 2014: Proceedings of the 14th SIAM International Conference on Data Mining 2014, Philadelphia, Pennsylvania, E1-1
Split-merge augmented Gibbs sampling for hierarchical dirichlet processes
S Rana, D Phung, S Venkatesh
(2013), pp. 546-557, PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings, Gold Coast, Queensland, E1
Exploiting side information in distance dependent Chinese restaurant processes for data clustering
C Li, D Phung, S Rana, S Venkatesh
(2013), pp. 1-6, ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, San Jose, California, E1
Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach
S Rana, D Phung, S Pham, S Venkatesh
(2012), pp. 1-8, ICVGIP 2012 : Proceedings of the 8th Indian Conference on Computer Vision, Graphics and Image Processing, Mumbai, India, E1
Multi-modal abnormality detection in video with unknown data segmentation
T Nguyen, D Phung, S Rana, D Pham, S Venkatesh
(2012), pp. 1322-1325, ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, Tsubuka Science City, Japan, E1
A unified tensor framework for face recognition
S Rana, W Liu, M Lazarescu, S Venkatesh
(2009), Vol. 42, pp. 2850-2862, Pattern recognition, Oxford, England, C1-1
Recognising faces in unseen modes: a tensor based approach
S Rana, W Liu, M Lazarescu, S Venkatesh
(2008), pp. 1-8, CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, E1-1
Efficient tensor based face recognition
S Rana, W Liu, M Lazarescu, S Venkatesh
(2008), pp. 1-4, ICPR 2008 : Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, Florida, E1-1
TRF: Learning Kernels with Tuned Random Features
Alistair Shilton, Sunil Gupta, Santu Rana, Arun Venkatesh, Svetha Venkatesh
(), Vol. 36, pp. 8286-8294, Proceedings of the AAAI Conference on Artificial Intelligence, E1
Funded Projects at Deakin
Australian Competitive Grants
ARC Research Hub for Digital Enhanced Living
Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr John Fouyaxis, Dr Kit Huckvale, Prof John Grundy, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Ms Sharon Grocott, Prof Deborah Parker, Dr Scott Barnett, Dr Tom McClean, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Mr Fernando Escorcia, Dr Gnana Bharathy
ARC Industrial Transformation Research Hubs
- 2021: $388,477
- 2020: $385,381
- 2019: $399,716
- 2018: $449,083
- 2017: $601,698
A Generic Framework for Verifying Machine Learning Algorithms
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran
ARC - Discovery Projects
- 2023: $122,467
- 2022: $125,478
- 2021: $122,286
Optimising treatments in mental health using AI
Prof Helen Christensen, Prof Svetha Venkatesh, Prof Henry Cutler, Ms Ros Knight, Dr Martin Laverty, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Thomas Quinn, Prof Rajesh Vasa, Prof Kon Mouzakis
MRFF (DISER) - Applied Artificial Intelligence Research in Health
- 2023: $690,221
- 2022: $999,770
- 2021: $1,431,416
Other Public Sector Funding
Al Algorithmic Assurance
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Anh Cat Le Ngo, Dr Phuoc Nguyen, Mr Stephan Jacobs, Dr Dang Nguyen
Department of Defence
- 2021: $248,140
- 2020: $208,820
- 2019: $80,640
Defence Applied Al Experiential CoLab
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran
DSTO Grant - Research - Defence Science & Technology Organisation
- 2021: $100,000
- 2020: $873,495
In relation to Assuring an off-the-shelf AI algorithm
Prof Sunil Gupta, A/Prof Truyen Tran, A/Prof Santu Rana, Prof Svetha Venkatesh, Dr Phuoc Nguyen, Mr Tiep-Trong Nguyen, Mr Stephan Jacobs
Defence Science and Technology Group - Department of Defence
- 2022: $85,000
- 2021: $168,034
Development of Bayesian optimisation tools for accelerated design and discovery of non-magnetic structural damping alloys
A/Prof Shannon Ryan, A/Prof Santu Rana, Dr Stewart Greenhill, Dr Julian Berk
Defence Science and Technology Group - Department of Defence
- 2022: $36,505
- 2021: $83,000
Coupled self-supervised learning and deep reasoning for improved processing of noisy and dynamic multimodal data from multiple sources.
A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Department of Defence
- 2022: $105,364
Coupled self-supervised learning and deep reasoning for improved processing of noisy and dynamic multimodal data from multiple sources
A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Defence Science and Technology Group - Department of Defence
- 2023: $423,000
Industry and Other Funding
ARC Research Hub for Digital Enhanced Living
Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr John Fouyaxis, Dr Kit Huckvale, Prof John Grundy, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Ms Sharon Grocott, Prof Deborah Parker, Dr Scott Barnett, Dr Tom McClean, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Mr Fernando Escorcia, Dr Gnana Bharathy
Unisono Pty Ltd, goAct, Uniting AgeWell, Aged Care & Housing Group Inc, Health Metrics, Black Dog Institute, NeoProducts Pty Ltd, Interrelate Limited, Cancer Council Victoria Grant - Research, Uniting NSW.ACT, Dementia Australia (Alzheimer's Australia) Vic Inc
- 2022: $793,130
- 2020: $553,025
- 2019: $378,745
iCetana - Phase 1 Examine and compare state-of-art methods in background/foreground separation
Prof Svetha Venkatesh, A/Prof Santu Rana, Prof Sunil Gupta, Dr Budhaditya Saha
iCetana Pty Ltd
- 2020: $50,000
- 2018: $100,000
- 2017: $200,000
Machine Learning for Design & Evalution of Complex Armour Systems.
A/Prof Shannon Ryan, A/Prof Santu Rana, Dr Julian Berk
Plasan Sasa Ltd
- 2023: $46,000
Other Funding Sources
The CRC-P for Advanced Hybrid Batteries
Prof Patrick Howlett, Prof Maria Forsyth, A/Prof Robert Kerr, Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana
Cooperative Research Centres (CRC) Projects Program, Department of Industry, Innovation and Science.
- 2023: $522,192
- 2022: $189,722
- 2021: $295,583
- 2020: $170,546
Supervisions
Buddhika Semage
Thesis entitled: Robust and Efficient Reinforcement Learning for Physics Tasks
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Arun Kumar Anjanapura Venkatesh
Thesis entitled: Accelerating Bayesian Optimisation with Advanced Kernel Learning Methods
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Majid Abdolshah
Thesis entitled: Multi-objective Bayesian Optimisation and Its Applications
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Ang (Leon) Yang
Thesis entitled: Scalable Bayesian Optimization with Sparse Gaussian Process Models
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Tinu Theckel Joy
Thesis entitled: Efficient Hyperparameter Tuning using Bayesian Optimization
Doctor of Philosophy (Information Technology), School of Information Technology
Haripriya Harikumar
Thesis entitled: Machine learning to fight addiction using social media
Doctor of Philosophy (Information Technology), School of Information Technology
Deepthi Praveenlal Kuttichira
Thesis entitled: Tackling Practical Challenges in Neural Network Model Deployment
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Tang Thanh Nguyen
Thesis entitled: On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Huu Phuc Luong
Thesis entitled: Bayesian Optimization for Discrete, Missing and Cost-sensitive Inputs
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Julian Maxwell Andrew Berk
Thesis entitled: A Distributional Approach towards Efficient and Versatile Bayesian Optimisation
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Anil Ramachandran
Thesis entitled: Harnessing Auxiliary Knowledge Towards Efficient Bayesian Optimisation
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Thanh Dai Nguyen
Thesis entitled: Addressing Practical Challenges of Bayesian Optimization
Doctor of Philosophy (Information Technology), School of Information Technology
Cheng Li
Thesis entitled: Exploiting side information in Bayesian nonparametric models and their applications
Doctor of Philosophy (Information Technology), School of Information Technology