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Results for: 2018

Multi-Target Optimisation via Bayesian Optimisation and Linear Programming

A. Shilton, S. Rana, S. Gupta, S. Venkatesh

2018 / UAI 2018: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence

GoGP: scalable geometric-based Gaussian process for online regression

T. Le, K. Nguyen, V. Nguyen, T. Nguyen, D. Phung

2018 / pp. 1 - 30 / Knowledge and Information Systems

Representation Learning in Complex Data via Pattern Discovery

D. Pham

2018 / Doctor of Philosophy (Information Technology), School of Information Technology

Machine learning to fight addiction using social media

H. Harikumar

2018 / Doctor of Philosophy (Information Technology), School of Information Technology

Selecting Optimal Source for Transfer Learning in Bayesian Optimisation

A. Ramachandran, S. Gupta, R. Santu, S. Venkatesh

2018 / pp. 42 - 56 / The 15th Pacific Rim International Conference on Artificial Intelligence

Exploration Enhanced Expected Improvement for Bayesian Optimization

J. Berk, V. Nguyen, S. Gupta, R. Santu, S. Venkatesh

2018 / pp. 621 - 637 / The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

Efficient Bayesian Optimisation Using Derivative Meta-Model

A. Yang, C. Li, S. Rana, S. Gupta, S. Venkatesh

2018 / pp. 252 - 264 / The 15th Pacific Rim International Conference on Artificial Intelligence

Exploiting Strategy-Space Diversity for Batch Bayesian Optimization

S. Gupta, A. Shilton, S. Rana, S. Venkatesh

2018 / pp. 538 - 547 / The 21st International Conference on Artificial Intelligence and Statistics

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 / The Journal of The Minerals, Metals and Materials Society (JOM)

A Privacy Preserving Bayesian Optimization with High Efficiency

N. Dai, S. Gupta, S. Rana, S. Venkatesh

2018 / pp. 543 - 555 / Pacific-Asia Conference on Knowledge Discovery and Data Mining

Predicting components for issue reports using deep learning with information retrieval

M. Choetkiertikul, H. Dam, T. Tran, T. Pham, A. Ghose

2018 / pp. 244 - 245 / Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Explainable software analytics

H. Dam, T. Tran, A. Ghose

2018 / pp. 53 - 56 / Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results

On catastrophic forgetting and mode collapse in Generative Adversarial Networks

H. Thanh-Tung, T. Tran, S. Venkatesh

2018 / ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models

Committee machine that votes for similarity between materials

D. Nguyen, T. Pham, V. Nguyen, T. Ho, T. Tran, K. Takahashi, H. Dam

2018 / Vol. 5 / IUCrJ

Neural reasoning for chemical-chemical interaction

T. Pham, T. Tran, S. Venkatesh

2018 / NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials

Automatic feature learning for predicting vulnerable software components

H. Dam, T. Tran, T. Pham, S. Ng, J. Grundy, A. Ghose

2018 / IEEE Transactions on Software Engineering

Making Sense of Pervasive Signals: a Machine Learning Approach

T. Binh

2018 / Doctor of Philosophy (Information Technology), School of Information Technology

Batch Normalized Deep Boltzmann Machines

V. Hung

2018 / Vol. 95, pp. 359 - 374 / Proceedings of The 10th Asian Conference on Machine Learning

Accelerating Experimental Design by Incorporating Experimenter Hunches

C. Li, S. Rana, S. Gupta, V. Nguyen, S. Venkatesh, A. Sutti, C. de, T. Slezak, M. Height, M. Mohammed, O.

2018 / pp. 257 - 266 / 2018 IEEE International Conference on Data Mining (ICDM)