Sunil Gupta
Cited by
Cited by
Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view
W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, ...
Journal of medical Internet research 18 (12), e323, 2016
Nonnegative shared subspace learning and its application to social media retrieval
SK Gupta, D Phung, B Adams, T Tran, S Venkatesh
Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
S Gupta, T Tran, W Luo, D Phung, RL Kennedy, A Broad, D Campbell, ...
BMJ open 4 (3), 2014
Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso
I Kamkar, SK Gupta, D Phung, S Venkatesh
Journal of biomedical informatics 53, 277-290, 2015
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
C Li, DR de Celis Leal, S Rana, S Gupta, A Sutti, S Greenhill, T Slezak, ...
Scientific reports 7 (1), 1-10, 2017
High dimensional bayesian optimization using dropout
C Li, S Gupta, S Rana, V Nguyen, S Venkatesh, A Shilton
arXiv preprint arXiv:1802.05400, 2018
High dimensional bayesian optimization with elastic gaussian process
S Rana, C Li, S Gupta, V Nguyen, S Venkatesh
International Conference on Machine Learning, 2883-2891, 2017
Differentially private random forest with high utility
S Rana, SK Gupta, S Venkatesh
2015 IEEE International Conference on Data Mining, 955-960, 2015
Regularized nonnegative shared subspace learning
SK Gupta, D Phung, B Adams, S Venkatesh
Data mining and knowledge discovery 26 (1), 57-97, 2013
Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes
T Nguyen, D Phung, S Gupta, S Venkatesh
2013 IEEE International Conference on Pervasive Computing and Communications …, 2013
A framework for feature extraction from hospital medical data with applications in risk prediction
T Tran, W Luo, D Phung, S Gupta, S Rana, RL Kennedy, A Larkins, ...
BMC bioinformatics 15 (1), 1-9, 2014
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
Hyperparameter tuning for big data using Bayesian optimisation
TT Joy, S Rana, S Gupta, S Venkatesh
2016 23rd International Conference on Pattern Recognition (ICPR), 2574-2579, 2016
Factorial multi-task learning: a bayesian nonparametric approach
S Gupta, D Phung, S Venkatesh
International conference on machine learning, 657-665, 2013
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
Multiple task transfer learning with small sample sizes
B Saha, S Gupta, D Phung, S Venkatesh
Knowledge and information systems 46 (2), 315-342, 2016
Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset
W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, D Phung, ...
PloS one 10 (5), e0125602, 2015
Connectivity, online social capital, and mood: A Bayesian nonparametric analysis
D Phung, SK Gupta, T Nguyen, S Venkatesh
IEEE transactions on multimedia 15 (6), 1316-1325, 2013
A Bayesian Nonparametric Joint Factor Model for Learning Shared and Individual Subspaces from Multiple Data Sources
SK Gupta, D Phung, S Venkatesh
SIAM Conference on Data Mining, 200-211, 2012
A flexible transfer learning framework for Bayesian optimization with convergence guarantee
TT Joy, S Rana, S Gupta, S Venkatesh
Expert Systems with Applications 115, 656-672, 2019
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