2016

  • Justin Bedő, Cheng Soon Ong, Multivariate Spearman's rho for Aggregating Ranks Using Copulas, JMLR 17(201):1−30, 2016
    PDF JMLR arXiv
  • Richard Nock, Aditya Krishna Menon, Cheng Soon Ong, A Scaled Bregman Theorem with Applications, NIPS 2016
    PDF NIPS arXiv spotlight video
  • Dawei Chen, Cheng Soon Ong, Lexing Xie, Learning Points and Routes to Recommend Trajectories, CIKM 2016
    PDF arXiv code
  • Dongwoo Kim, Lexing Xie, Cheng Soon Ong, Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches, CIKM 2016
    PDF arXiv code data
  • Young Lee, Kar Wai Lim, Cheng Soon Ong, Hawkes Processes with Stochastic Excitations, ICML 2016
    PDF ICML
  • Aditya Menon, Cheng Soon Ong, Linking losses for density ratio and class-probability estimation, ICML 2016
    PDF ICML
  • André Kahles, Cheng Soon Ong, Yi Zhong, and Gunnar Rätsch, SplAdder: Identification, quantification and testing of alternative splicing events from RNA-Seq data, Bioinformatics, 32(12), pp. 1840-1847, 2016
    PDF journal github
  • Gaëlle Loosli, Stéphane Canu, Cheng Soon Ong, Learning SVM in Krein Spaces, IEEE Transactions of Pattern Analysis and Machine Intelligence, 38(6), pp.1204-1216, 2016.
    PDF DOI code

2015

  • Hayley Reynolds, Scott Williams, Alan Zhang, Rajib Chakravorty, David Rawlinson, Cheng Soon Ong, Miguel Esteva, Catherine Mitchell, Bimal Parameswaran, Mary Finnegan, Gary Liney and Annette Haworth, Development of a registration framework to validate MRI with histology for prostate focal therapy, Medical Physics 42(12), pp. 7078-7089 (2015); doi: 10.1118/1.4935343
    PDF journal
  • Aditya Krishna Menon, Brendan van Rooyen, Cheng Soon Ong, Robert C. Williamson Learning from Corrupted Binary Labels via Class-Probability Estimation International Conference on Machine Learning, 2015
    PDF code
  • Qiao Wang, Sylvia Young, Aaron Harwood, Cheng Soon Ong Discriminative Concept Learning Network: Reveal High-level Differential Concepts from Shallow Architecture International Joint Conference on Neural Networks, 2015
    PDF code and data
  • Cheng Soon Ong, Wray Buntine, Tu-Bao Ho, Masashi Sugiyama, Geoffrey I. Webb, Special issue of selected papers of ACML 2013 Machine Learning, vol 99, no. 2, 2015
    Intro special issue

2014

  • Arash Kianianmomeni, Cheng Soon Ong, Gunnar Rätsch, Armin Hallmann, Genome-wide analysis of alternative splicing in Volvox carteri BMC Genomics, 2014, 15:1117
    PDF BMC link
  • Geoff Macintyre, Antonio Jimeno Yepes, Cheng Soon Ong, Karin Verspoor, Associating disease-related genetic variants in intergenic regions to the genes they impact PeerJ 2:e639 http://dx.doi.org/10.7717/peerj.639
    PDF PeerJ
  • Francesco Dinuzzo, Cheng Soon Ong, Kenji Fukumizu, Output Kernel Learning Methods Chapter 16: Regularization, Optimization, Kernels, and Support Vector Machines, 2014, pp. 359-370, CRC Press
    preprint CRC press
  • Justin Bedő, David Rawlinson, Benjamin Goudey, Cheng Soon Ong, Stability of bivariate GWAS biomarker detection PLoS ONE, 9(4), e93319, DOI: 10.1371/journal.pone.0093319
    PDF supplement PLoS ONE raw results (39GB) raw results (torrent)
  • Mikio L. Braun, Cheng Soon Ong, Open Science in Machine Learning. Book chapter in Implementing Reproducible Research, 2014, CRC Press
    PDF book
  • Sharon Wulff, Cheng Soon Ong, Analytic center cutting plane method for multiple kernel learning Annals of Mathematics and Artificial Intelligence: Volume 69, Issue 3 (2014), Page 225-241
    PDF journal

2013

  • Fan Shi, Cheng Soon Ong, Christopher Leckie. Applications of Class-Conditional Conformal Predictor in Multi-Class Classification International Conference on Machine Learning and Applications, 2013
    PDF
  • Francesco Dinuzzo, Cheng Soon Ong, Kenji Fukumizu. Output Kernel Learning Methods International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications, 2013
    PDF
  • Joaquin Vanschoren, Mikio Braun, Cheng Soon Ong, Open Science in Machine Learning, Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG) 2013, Modena, Italy
    PDF scienceopen.com
  • Alberto Giovanni Busetto, Alain Hauser, Gabriel Krummenacher, Mikael Sunnåker, Sotiris Dimopoulos, Cheng Soon Ong, Jörg Stelling and Joachim M. Buhmann. Near-optimal experimental design for model selection in systems biology Bioinformatics, 29 (20): 2625-2632. doi:10.1093/bioinformatics/btt436
    PDF supp journal link software
  • Benjamin Goudey, David Rawlinson, Qiao Wang, Fan Shi, Herman Ferra, Richard M Campbell, Linda Stern, Michael T Inouye, Cheng Soon Ong, Adam Kowalczyk. GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS BMC Genomics 2013, vol 14(Suppl 3):S10
    PDF BMC link supp web service
  • Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Peter J. Wild, Niels J. Rupp, Joachim M. Buhmann. TMARKER: A free software toolkit for histopathological cell counting and staining estimation, Journal of Pathology Informatics, 2013, vol 4, issue 2.
    PDF JPI link web server
  • Gabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann, Ellipsoidal Multiple Instance Learning. International Conference on Machine Learning, 2013
    PDF supplement code

2012

  • Kay H. Brodersen, Cristoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong, Joachim M. Buhmann, Klass E. Stephan, Mixed-effects inference on classification performance in hierarchical datasets. Journal of Machine Learning Research. 13(Nov):3133-3176, 2012.
    PDF JMLR link
  • Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Peter J. Wild, Niels J. Rupp, Joachim M. Buhmann. TMARKER: A free software toolkit for histopathological cell counting and staining estimation, Histopathology Image Analysis (HIMA) Workshop at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2012).
    preprint
  • Cheng Soon Ong, Le Thi Hoai An. Learning sparse classifiers with Difference of Convex functions Algorithms, Optimization Methods and Software, vol. 28, issue 4, pp. 830--854
    preprint journal
  • Patrick Pletscher, Cheng Soon Ong. Part & Clamp: Efficient Structured Output Learning (AISTATS) JMLR W&CP 22: 877-885, 2012.
    pdf proceedings

2011

  • Andreas Krause, Cheng Soon Ong. Contextual Gaussian Process Bandit Optimization. Advances in Neural Information Processing, 2011.
    pdf supplement MHC data
  • Kay H. Brodersen, Thomas M. Schofield, Alexander P. Leff, Cheng Soon Ong, Ekaterina I. Lomakina, Joachim M. Buhmann, and Klaas E Stephan. Generative embedding for model-based classification of fMRI data. PLoS Computational Biology 7(6): e1002079, 2011.
    doi:10.1371/journal.pcbi.1002079   pdf
  • Francesco Dinuzzo, Cheng Soon Ong, Peter Gehler, and Gianluigi Pillonetto. Learning Output Kernels with Block Coordinate Descent. Proceedings of the International Conference on Machine Learning, 2011.
    pdf   software and data
  • Kay H. Brodersen, Florent Haiss, Cheng Soon Ong, Fabienne Jung, Marc Tittgemeyer, Joachim M. Buhmann, Bruno Weber and Klaas E. Stephan. Model-based feature construction for multivariate decoding. NeuroImage, Volume 56, Issue 2, Pages 601-615, 2011.
    DOI   pdf   supplement   data

2010

  • Patrick Pletscher, Cheng Soon Ong and Joachim M. Buhmann. Entropy and margin maximization for structured output learning. Proceedings of European Conference on Machine Learning, 2010.
    pdf
  • Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann. Computational TMA analysis and cell nucleus classification of renal cell carcinoma. Proceedings of the German Pattern Recognition Society (DAGM), 2010.
    pdf
  • Kay H. Brodersen, Cheng Soon Ong, Klaas E. Stephan, Joachim M. Buhmann. The balanced accuracy and its posterior distribution. Proceedings of the 20th International Conference on Pattern Recognition, 2010.
    pdf   software
  • Kay H. Brodersen, Cheng Soon Ong, Klaas E. Stephan, Joachim M. Buhmann. The binormal assumption on precision-recall curves. Proceedings of the 20th International Conference on Pattern Recognition, 2010.
    pdf   software

2009

  • Gabriele Schweikert, Alexander Zien, Georg Zeller, Jonas Behr, Christoph Dieterich, Cheng Soon Ong, Petra Philips, Fabio De Bona, Lisa Hartmann, Anja Bohlen, Nina Krüger, Sören Sonnenburg, and Gunnar Rätsch. mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research, 19:2133--2143, 2009.
    pdf   Genome Research link
  • Alberto Giovanni Busetto, Cheng Soon Ong and Joachim M. Buhmann. Optimized Expected Information Gain for Nonlinear Dynamical Systems. In Proceedings of the International Conference on Machine Learning, pages 97--104, 2009.
    pdf
  • Gabriele Schweikert, Jonas Behr, Alexander Zien, Georg Zeller, Cheng Soon Ong, Sören Sonnenburg and Gunnar Rätsch. mGene.web: a web service for accurate computational gene finding. Nucleic Acids Research, Volume 37, Web Server Issue, 2009.
    http://www.mgene.org/web
    pdf   NAR link
  • Patrick Pletscher, Cheng Soon Ong and Joachim M. Buhmann. Spanning Tree Approximations for Conditional Random Fields. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR W&CP 5, pages 408--415, 2009.
    http://www.pletscher.org/academics/projects/crfspanning/
    pdf

2008

  • Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, and Gunnar Rätsch. Support vector machines and kernels for computational biology. PLoS Computational Biology 4(10), e1000173, 2008.
    http://svmcompbio.tuebingen.mpg.de
    http://easysvm.org
    pdf   PLoS Computational Biology link
  • Cheng Soon Ong and Alexander Zien. An automated combination of kernels for predicting protein subcellular localization. In K. A. Crandall and J. Lagergren, editors, Proceedings of the 8th Workshop on Algorithms in Bioinformatics (WABI 2008), pages 186–197. Springer, 2008.
    data
    pdf   supplement

2007

  • Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, and Robert C. Williamson. The need for open source software in machine learning. Journal of Machine Learning Research, 8:2443–2466, 2007.
    http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html
    http://mloss.org
    pdf
  • Alexander Zien and Cheng Soon Ong. Multiclass multiple kernel learning. In Proceedings of the International Conference on Machine Learning, pages 1191–1198, 2007.
    software data
    pdf
  • Alexander Zien, Fabio De Bona, and Cheng Soon Ong. Training and approximation of a primal multiclass support vector machine. In 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007), 2007.
    pdf
  • Uta Schulze, Bettina Hepp, Cheng Soon Ong, and Gunnar Rätsch. PALMA: mRNA to genome alignments using large margin algorithms. Bioinformatics, 23(15):1892–1900, 2007.
    pdf supplement

2006

  • Gunnar Rätsch, Bettina Hepp, Uta Schulze, and Cheng Soon Ong. Palma: Perfect alignments using large margin algorithms. In D. Huson, O. Kohlbacher, A. Lupas, K. Nieselt, and A. Zell, editors, German Conference on Bioinformatics, volume P-83 of Lecture Notes in Informatics, pages 104–113, Tübingen, Germany, September 2006. Springer.
    pdf

2005

  • Stéphane Canu, Cheng Soon Ong, and Xavier Mary. Splines with non positive kernels. In 5th International ISAAC Congress, 2005.
    pdf
  • Cheng Soon Ong. Kernels: Regularization and Optimization. PhD thesis, The Australian National University, 2005.
    pdf
    ANU link
  • Cheng Soon Ong, Alexander J. Smola, and Robert C. Williamson. Learning the kernel with hyperkernels. Journal of Machine Learning Research, 6:1043–1071, 2005.
    pdf
  • Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S.V.N. Vishwanathan, Alexander J. Smola, and Hans-Peter Kriegel. Protein function prediction via graph kernels. In Proceedings of the International Conference on Intelligent Systems for Molecular Biology, 2005.
    pdf

2004

  • Cheng Soon Ong, Xavier Mary, Stéphane Canu, and Alexander J. Smola. Learning with non-positive kernels. In International Conference on Machine Learning, pages 639–646, 2004.
    pdf
  • Cheng Soon Ong and Alexander J. Smola. Machine learning with hyperkernels. In International Conference of Machine Learning, pages 568–575, 2003.
    pdf

2003

  • Cheng Soon Ong, Alexander J. Smola, and Robert C. Williamson. Hyperkernels. In Advances in Neural Information Processing Systems 15, pages 495–502, 2003.
    pdf

2002 and earlier

  • Fadhli Wong Mohd. Hasan Wong, Ainil Sufreena Mohd. Supian, Ahmad Faris Ismail, Weng Kin Lai, and Cheng Soon Ong. Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm. In Proceesdings of the 35th Asilomar Conference on Signals,Systems & Computers, 2001.
  • S.Y. Tai, C.S. Ong, and Noor Aida Abdullah. On designing an automated Malaysian stemmer for the Malay language. In Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages, 2000.
    ps
  • Cheng Soon Ong and Weng Kin Lai. Enhanced password authentication through typing biometrics with k-means clustering algorithm. In World Automation Congress, June 2000.
    pdf
  • Cheng Soon Ong, Fadhli Wong, and Weng Kin Lai. A high resolution and accurate pentium based timer. In National Real Time Technology and Applications Symposium, Malaysia, 2000.
    pdf
  • Cheng Soon Ong. Knowledge discovery in databases: An information retrieval perspective. Malaysian Journal of Computer Science, 13(2), December 2000.
    ps
  • L.K. Maisuria, C.S. Ong, and W.K. Lai. A comparison of artificial neural networks and cluster analysis for typing biometrics authentication. In International Joint Conference on Neural Networks, 1999.
    pdf