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Machine learning for a better world
Machine learning is out to
the world. Much of what previously programmed manually can now be
learned through examples.
Areas:
- Explainable AI: Machine
learning is often criticised as black-box which is perhaps the most
critical point that blocks wide adoption. This research aims to improve
the situation by creating a transparent-box.
- Recomemender
systems: The
goal of a recommender system is to deliver right services to right
users. Most existing work is
rather ad-hoc and ignores complex nature of the
data. Research topics include modeling choices, discovering
hidden patterns, incorporating contexts and side-information, social
networks, multiple-domains, product hierarchies, as well as
correlations between actors and items.
- Automated software engineering: Software is eating the world. Automated software engineering helps improving software development with
better risk estimation, lower cost and higher code quality.
- Anomaly detection: detects unknown unknowns (e.g., outliers and anomalies).
- Process modeling:
Processes are logics that underpin businesses, factories and
operations. ML offers new ways for discovery of actionable insights
from event log, as well as process prediction, optimization and
planning.
Publications
Explainable AI
- Explainable Software Analytics, HK Dam, T Tran, A Ghose, ICSE 2018 New Ideas and Emerging Results
- Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Health and Biomedical Informatics, 2017, Doi: 10.1109/JBHI.2016.2633963.
- Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data, Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh, Conference on Machine Learning in Healthcare, LA, USA Aug 2016.
- Stabilizing
Linear Prediction Models using Autoencoder, Shivapratap
Gopakumara, Truyen Tran,
Dinh Phung, Svetha Venkatesh, International
Conference on Advanced Data Mining and Applications (ADMA
2016).
- Stabilizing Sparse Cox Model
using Statistic and Semantic Structures in Electronic Medical Records.
Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen Tran, Dinh
Phung, and Svetha Venkatesh, PAKDD'15,
HCM City, Vietnam, May 2015.
- Stabilizing
high-dimensional
prediction models using feature graphs, Shivapratap
Gopakumar, Truyen Tran,
Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh, IEEE Journal of Biomedical and
Health Informatics, 2014
DOI:10.1109/JBHI.2014.2353031S
- Stabilizing
sparse Cox model using clinical structures in electronic medical records,
S Gopakumar, Truyen Tran,
D Phung, S Venkatesh, 2nd
International Workshop on Pattern Recognition for Healthcare Analytics,
August 2014
- HealthMap:
A visual platform for patient suicide risk review, S Rana, W
Luo, Truyen Tran,
D Phung, S Venkatesh, R Harvey, HISA
Big Data, Melbourne April, 2014
- Stabilized
sparse ordinal regression for medical risk stratification, Truyen Tran, Dinh
Phung, Wei Luo, and Svetha Venkatesh, Knowledge and Information Systems,
2014, DOI: 10.1007/s10115-014-0740-4.
Recommender systems
- Preference Relation-based Markov Random Fields in Recommender Systems, Shaowu Liu, Gang Li,Truyen Tran, Jiang Yuan, Machine Learning, April 2017, Volume 106, Issue 4, pp 523–546. This is an extension of the ACML'15 paper.
- Neural Choice by Elimination via Highway Networks, Truyen Tran, Dinh Phung and Svetha Venkatesh, PAKDD workshop on Biologically Inspired Techniques for Data Mining (BDM'16), April 19-22 2016, Auckland, NZ.
- Collaborative filtering
via sparse Markov random fields, Truyen Tran, Dinh
Phung, Svetha Venkatesh, Information
Sciences, doi:10.1016/j.ins.2016.06.027.
- Preference
Relation-based Markov Random Fields, Shaowu Liu,
Gang Li,Truyen Tran,
Jiang Yuan, ACML'15, November 20-22, 2015, Hong Kong.
- Modelling
Human Preferences for Ranking and Collaborative Filtering: A
Probabilistic Ordered Partition Approach, Truyen
Tran,
D.
Phung, and S. Venkatesh (extension of the SDM'11
paper). Knowledge
and Information
Systems . May 2015
- Ordinal
random fields for recommender systems, Shaowu Liu,
Truyen Tran,
Gang Li, Yuan Jiang, ACML'14,
Nha Trang,
Vietnam, Nov 2014.
- Latent
patient profile modelling and
applications with Mixed-Variate Restricted Boltzmann Machine,
Tu
D. Nguyen, Truyen Tran,
D. Phung, and S. Venkatesh, In
Proc. of 17th
Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD’13), Gold Coast, Australia, April 2013.
- Thurstonian
Boltzmann machines: Learning from multiple inequalities,
Truyen Tran,
D.
Phung, and S. Venkatesh, In
Proc. of 30th
International Conference in Machine Learning (ICML’13),
Atlanta, USA, June, 2013.
- Learning
from Ordered Sets and
Applications in Collaborative Ranking,
Truyen Tran,
Dinh Phung and
Svetha Venkatesh, in Proc.
of. the 4th Asian Conference on
Machine Learning (ACML2012), Singapore, Nov 2012.
- Cumulative
Restricted
Boltzmann Machines for Ordinal Matrix Data Analysis,
Truyen Tran,
Dinh Phung and
Svetha Venkatesh, in Proc.
of. the 4th Asian Conference on
Machine Learning (ACML2012), Singapore, Nov 2012.
- A
Sequential Decision Approach
to Ordinal Preferences in Recommender Systems, Truyen
Tran, Dinh Phung, Svetha Venkatesh, in Proc.
of 25-th Conference on Artificial Intelligence (AAAI-12),
Toronto,
Canada, July 2012.
- Mixed-Variate
Restricted
Boltzmann Machines, Truyen
Tran, Dinh Phung and Svetha Venkatesh, in Proc.
of. the 3rd Asian Conference on Machine Learning (ACML2011),
Taoyuan, Taiwan, Nov 2011.
- Probabilistic
Models over Ordered Partitions with Applications in Document Ranking
and Collaborative Filtering
T. Truyen,
D.
Phung, and S. Venkatesh, in Proc.
of SIAM Int. Conf. on Data
Mining (SDM11), April, Arizona, USA, 2011.
- Ordinal
Boltzmann Machines for
Collaborative Filtering. Truyen
Tran, Dinh Q. Phung and Svetha Venkatesh. In
Proc. of 25th
Conference on Uncertainty in Artificial Intelligence,
June, 2009, Montreal, Canada. Runner-up
for the best paper award.
- Preference
Networks:
probabilistic models for recommendation systems, Truyen
Tran, Dinh Q. Phung and Svetha Venkatesh, In Proc.
of 6th Australasian Data Mining Conference: AusDM 2007,
3-4
Dec, Gold Coast, Australia.
Automated software engineering
- Towards effective AI-powered agile project management, HK Dam, T Tran, J Grundy, A Ghose, Y Kamei, ICSE'19 New Ideas and Emerging Results
- Lessons learned from using a deep tree-based model for software defect prediction in practice, HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim, MSR'19
- Automatic feature learning for predicting vulnerable software components, Hoa Khanh Dam, Truyen Tran, Trang Pham, Shien
Wee Ng, John Grundy, Aditya Ghose, IEEE Transactions on Software Engineering, 2019
- Predicting components for issue reports using deep learning with information retrieval, M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, International Conference on Software Engineering (ICSE'18) - Poster Track
- Explainable Software Analytics, HK Dam, T Tran, A Ghose, ICSE 2018 New Ideas and Emerging Results
- A
deep learning model for estimating story points, M
Choetkiertikul, HK Dam, T
Tran, T Pham, A Ghose, T Menzies, IEEE Transactions on Software Engineering, 2018
- Graph Classification via Deep Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).
- Predicting delivery capability in iterative software development, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, John Grundy, IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2017.2693989, April 2017.
- Predicting the delay of issues with due dates in software
projects, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran,
Aditya Ghose, Empirical Software Engineering, doi:10.1007/s10664-016-9496-7, Feb 2017
- Column
Networks for Collective Classification, T Pham, T Tran, D Phung, S
Venkatesh, AAAI'17
- DeepSoft:
A vision for a deep model of software, Hoa Khanh Dam, Truyen Tran, John
Grundy and Aditya Ghose, FSE,
Vision and Reflections Track, 2016.
- A
deep language model for software code, Hoa Khanh Dam, Truyen Tran and
Trang Pham, FSE
Workshop on NL+SE 2016.
- Who will answer my
question on Stack Overflow?, Morakot
Choetkiertikul, Daniel Avery, Hoa Khanh Dam, Truyen Tran and
Aditya Ghose, 24th
Australasian Software Engineering Conference (ASWEC 2015), Adelaide,
Australia, September 28 - October 1, 2015.
- Predicting delays in
software projects using networked classification,
Morakot Choetikertikul, Hoa Khanh Dam, Truyen Tran, Aditya
Ghose, 30th IEEE/ACM
International Conference on Automated Software Engineering,
November 9–13, 2015 Lincoln, Nebraska, USA.
- Characterization
and
prediction of issue-related risks in software projects,
Morakot
Choetikertikul, Hoa Khanh Dam, Truyen
Tran, Aditya Ghose, MSR'15,
May
16–17. Florence, Italy. Winner of
ACM SIGSOFT Distinguished Paper Award.
Anomaly detection
- Learning regularity in skeleton trajectories for anomaly detection in videos, Romero Morais, Vuong Le, Budhaditya Saha, Truyen Tran, Moussa Reda Mansour, Svetha Venkatesh, CVPR'19.
- Energy-Based Anomaly Detection for Mixed Data, Kien Do, Truyen Tran, Svetha Venkatesh, Knowledge and Information Systems, 2018.
- Multilevel Anomaly Detection for Mixed Data, K Do, T Tran, S Venkatesh, arxiv preprint arxiv 1610.06249.
- Outlier Detection on Mixed-Type Data: An Energy-based Approach, K Do, T Tran, D Phung, S Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016). Best student runner-up paper award.
Process modeling
- Memory–augmented
neural
retworks for predictive process analytics, Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta, arXiv
preprint
arXiv:1802.00938.
- Forecasting Daily
Patient Outflow From a Ward Having no Real-Time Clinical Data,
Shivapratap Gopakumara, Truyen
Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, JMIR,
Vol 4, No 3 (2016): Jul-Sept.
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