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2020 – today
- 2024
[c42]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. AAAI 2024: 20559-20566- [i27]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. CoRR abs/2402.09608 (2024) - [i26]Sevvandi Kandanaarachchi, Cheng Soon Ong:
Graphons of Line Graphs. CoRR abs/2409.01656 (2024) - [i25]Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla:
Variational Search Distributions. CoRR abs/2409.06142 (2024) - 2023
- [j21]Ransalu Senanayake, Daniel J. Fremont, Mykel J. Kochenderfer, Alessio R. Lomuscio
, Dragos D. Margineantu, Cheng Soon Ong:
Guest Editorial: Special issue on robust machine learning. Mach. Learn. 112(8): 2787-2789 (2023) - [j20]Russell Tsuchida, Cheng Soon Ong:
Stochastic gradient updates yield deep equilibrium kernels. Trans. Mach. Learn. Res. 2023 (2023) - [c41]Russell Tsuchida, Cheng Soon Ong:
Deep equilibrium models as estimators for continuous latent variables. AISTATS 2023: 1646-1671 - [c40]Alasdair Tran, Alexander Patrick Mathews, Lexing Xie, Cheng Soon Ong:
Factorized Fourier Neural Operators. ICLR 2023 - [c39]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Squared Neural Families: A New Class of Tractable Density Models. NeurIPS 2023 - [i24]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Squared Neural Families: A New Class of Tractable Density Models. CoRR abs/2305.13552 (2023) - [i23]Suk Yee Yong, Cheng Soon Ong:
Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction. CoRR abs/2307.04993 (2023) - 2022
- [c38]Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong:
Gaussian Process Bandits with Aggregated Feedback. AAAI 2022: 9074-9081 - [c37]Russell Tsuchida, Suk Yee Yong, Mohammad Ali Armin, Lars Petersson, Cheng Soon Ong:
Declarative nets that are equilibrium models. ICLR 2022 - [d1]Qiao Wang, Sylvia Young, Aaron Harwood, Cheng Soon Ong:
Discriminative Concept Learning Network: Sample Source Code and Supplement (Project Archive). IEEE DataPort, 2022
- [i22]Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong:
Two-Stage Neural Contextual Bandits for Personalised News Recommendation. CoRR abs/2206.14648 (2022) - [i21]Russell Tsuchida, Cheng Soon Ong:
Deep equilibrium models as estimators for continuous latent variables. CoRR abs/2211.05943 (2022) - 2021
- [j19]Ziad Al Bkhetan, Gursharan Chana, Cheng Soon Ong, Benjamin Goudey, Kotagiri Ramamohanarao:
eQTLHap: a tool for comprehensive eQTL analysis considering haplotypic and genotypic effects. Briefings Bioinform. 22(5) (2021) - [c36]Mengyan Zhang, Cheng Soon Ong:
Quantile Bandits for Best Arms Identification. ICML 2021: 12513-12523 - [c35]Alasdair Tran, Alexander Patrick Mathews, Cheng Soon Ong, Lexing Xie:
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series. WWW 2021: 730-742 - [i20]Alasdair Tran, Alexander Patrick Mathews, Cheng Soon Ong, Lexing Xie:
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series. CoRR abs/2102.07289 (2021) - [i19]Alasdair Tran, Alexander Patrick Mathews, Lexing Xie, Cheng Soon Ong:
Factorized Fourier Neural Operators. CoRR abs/2111.13802 (2021) - [i18]Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong:
Gaussian Process Bandits with Aggregated Feedback. CoRR abs/2112.13029 (2021) - 2020
- [i17]Mengyan Zhang, Cheng Soon Ong:
Quantile Bandits for Best Arms Identification with Concentration Inequalities. CoRR abs/2010.11568 (2020)
2010 – 2019
- 2019
- [c34]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
Costs and Benefits of Fair Representation Learning. AIES 2019: 263-270 - [c33]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge blunts Bayes: Hardness Results for Adversarial Training. ICML 2019: 1406-1415 - [c32]Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. NeurIPS 2019: 2251-2260 - [i16]Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon:
Cold-start Playlist Recommendation with Multitask Learning. CoRR abs/1901.06125 (2019) - [i15]Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama:
New Tricks for Estimating Gradients of Expectations. CoRR abs/1901.11311 (2019) - 2018
- [j18]Alasdair Tran, Cheng Soon Ong, Christian Wolf:
Combining active learning suggestions. PeerJ Comput. Sci. 4: e157 (2018) - [j17]Gabriel Krummenacher, Cheng Soon Ong, Stefan Koller, Seijin Kobayashi, Joachim M. Buhmann:
Wheel Defect Detection With Machine Learning. IEEE Trans. Intell. Transp. Syst. 19(4): 1176-1187 (2018) - [c31]Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun:
Representation Learning of Compositional Data. NeurIPS 2018: 6680-6690 - [i14]Finnian Lattimore, Cheng Soon Ong:
A Primer on Causal Analysis. CoRR abs/1806.01488 (2018) - [i13]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge beats Bayes: Hardness Results for Adversarial Training. CoRR abs/1806.02977 (2018) - [i12]Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon:
Cold-start playlist recommendation with multitask learning. PeerJ Prepr. 6: e27383 (2018) - 2017
- [j16]Kevin D. Murray, Christfried Webers, Cheng Soon Ong, Justin O. Borevitz, Norman Warthmann:
kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity. PLoS Comput. Biol. 13(9) (2017) - [c30]Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong:
Revisiting revisits in trajectory recommendation. CitRec@RecSys 2017: 2:1-2:6 - [c29]Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy:
PathRec: Visual Analysis of Travel Route Recommendations. RecSys 2017: 364-365 - [i11]Dawei Chen, Lexing Xie, Aditya Krishna Menon, Cheng Soon Ong:
Structured Recommendation. CoRR abs/1706.09067 (2017) - [i10]Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy:
PathRec: Visual Analysis of Travel Route Recommendations. CoRR abs/1707.01627 (2017) - [i9]Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong:
Revisiting revisits in trajectory recommendation. CoRR abs/1708.05165 (2017) - [i8]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
Provably Fair Representations. CoRR abs/1710.04394 (2017) - 2016
- [j15]André Kahles, Cheng Soon Ong, Yi Zhong, Gunnar Rätsch:
SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data. Bioinform. 32(12): 1840-1847 (2016) - [j14]Justin Bedo, Cheng Soon Ong:
Multivariate Spearman's rho for Aggregating Ranks Using Copulas. J. Mach. Learn. Res. 17: 201:1-201:30 (2016) - [j13]Gaëlle Loosli, Stéphane Canu, Cheng Soon Ong:
Learning SVM in Kreĭn Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 38(6): 1204-1216 (2016) - [c28]Dawei Chen, Cheng Soon Ong, Lexing Xie:
Learning Points and Routes to Recommend Trajectories. CIKM 2016: 2227-2232 - [c27]Dongwoo Kim, Lexing Xie, Cheng Soon Ong:
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches. CIKM 2016: 2257-2262 - [c26]Young Lee, Kar Wai Lim, Cheng Soon Ong:
Hawkes Processes with Stochastic Excitations. ICML 2016: 79-88 - [c25]Aditya Krishna Menon, Cheng Soon Ong:
Linking losses for density ratio and class-probability estimation. ICML 2016: 304-313 - [c24]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. NIPS 2016: 19-27 - [i7]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. CoRR abs/1607.00360 (2016) - [i6]Dongwoo Kim, Lexing Xie, Cheng Soon Ong:
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches. CoRR abs/1608.05921 (2016) - [i5]Dawei Chen, Cheng Soon Ong, Lexing Xie:
Learning Points and Routes to Recommend Trajectories. CoRR abs/1608.07051 (2016) - [i4]Young Lee, Kar Wai Lim, Cheng Soon Ong:
Hawkes Processes with Stochastic Excitations. CoRR abs/1609.06831 (2016) - [i3]Daniel McNamara, Cheng Soon Ong, Robert C. Williamson:
A Modular Theory of Feature Learning. CoRR abs/1611.03125 (2016) - 2015
- [j12]Cheng Soon Ong, Wray L. Buntine, Tu Bao Ho, Masashi Sugiyama, Geoffrey I. Webb:
Introduction: special issue of selected papers of ACML 2013. Mach. Learn. 99(2): 165-167 (2015) - [c23]Aditya Krishna Menon, Brendan van Rooyen, Cheng Soon Ong, Bob Williamson:
Learning from Corrupted Binary Labels via Class-Probability Estimation. ICML 2015: 125-134 - [c22]Qiao Wang, Sylvia Young, Aaron Harwood, Cheng Soon Ong:
Discriminative concept learning network: Reveal high-level differential concepts from shallow architecture. IJCNN 2015: 1-9 - 2014
- [c21]Hayley M. Reynolds, Scott Williams, Alan M. Zhang, Cheng Soon Ong, David Rawlinson, Rajib Chakravorty, Catherine Mitchell, Annette Haworth:
Cell density in prostate histopathology images as a measure of tumor distribution. Digital Pathology 2014: 90410S - [i2]Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong:
Open science in machine learning. CoRR abs/1402.6013 (2014) - [i1]Justin Bedo, Cheng Soon Ong:
Multivariate Spearman's rho for aggregating ranks using copulas. CoRR abs/1410.4391 (2014) - 2013
- [j11]Sharon Wulff, Cheng Soon Ong:
Analytic center cutting plane method for multiple kernel learning. Ann. Math. Artif. Intell. 69(3): 225-241 (2013) - [j10]Alberto Giovanni Busetto, Alain Hauser, Gabriel Krummenacher, Mikael Sunnåker, Sotiris Dimopoulos, Cheng Soon Ong, Jörg Stelling, Joachim M. Buhmann:
Near-optimal experimental design for model selection in systems biology. Bioinform. 29(20): 2625-2632 (2013) - [j9]Cheng Soon Ong, Le Thi Hoai An:
Learning sparse classifiers with difference of convex functions algorithms. Optim. Methods Softw. 28(4): 830-854 (2013) - [c20]Gabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann:
Ellipsoidal Multiple Instance Learning. ICML (2) 2013: 73-81 - [c19]Fan Shi, Cheng Soon Ong, Christopher Leckie:
Applications of Class-Conditional Conformal Predictor in Multi-class Classification. ICMLA (1) 2013: 235-239 - [p1]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma. Similarity-Based Pattern Analysis and Recognition 2013: 219-245 - [e1]Cheng Soon Ong, Tu Bao Ho:
Asian Conference on Machine Learning, ACML 2013, Canberra, ACT, Australia, November 13-15, 2013. JMLR Workshop and Conference Proceedings 29, JMLR.org 2013 [contents] - 2012
- [j8]Kay Henning Brodersen, Christoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong, Joachim M. Buhmann, Klaas E. Stephan:
Bayesian mixed-effects inference on classification performance in hierarchical data sets. J. Mach. Learn. Res. 13: 3133-3176 (2012) - [c18]Patrick Pletscher, Cheng Soon Ong:
Part & Clamp: Efficient Structured Output Learning. AISTATS 2012: 877-885 - 2011
- [j7]Kay Henning Brodersen, Florent Haiss, Cheng Soon Ong, Fabienne Jung, Marc Tittgemeyer, Joachim M. Buhmann, Bruno Weber, Klaas E. Stephan:
Model-based feature construction for multivariate decoding. NeuroImage 56(2): 601-615 (2011) - [j6]Kay Henning Brodersen, Thomas M. Schofield, Alexander P. Leff, Cheng Soon Ong, Ekaterina I. Lomakina, Joachim M. Buhmann, Klaas E. Stephan:
Generative Embedding for Model-Based Classification of fMRI Data. PLoS Comput. Biol. 7(6) (2011) - [c17]Francesco Dinuzzo, Cheng Soon Ong, Peter V. Gehler, Gianluigi Pillonetto:
Learning Output Kernels with Block Coordinate Descent. ICML 2011: 49-56 - [c16]Andreas Krause, Cheng Soon Ong:
Contextual Gaussian Process Bandit Optimization. NIPS 2011: 2447-2455 - 2010
- [c15]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma. DAGM-Symposium 2010: 202-211 - [c14]Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, Joachim M. Buhmann:
The Balanced Accuracy and Its Posterior Distribution. ICPR 2010: 3121-3124 - [c13]Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, Joachim M. Buhmann:
The Binormal Assumption on Precision-Recall Curves. ICPR 2010: 4263-4266 - [c12]Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
Entropy and Margin Maximization for Structured Output Learning. ECML/PKDD (3) 2010: 83-98
2000 – 2009
- 2009
- [j5]Gabriele Beate Schweikert, Jonas Behr, Alexander Zien, Georg Zeller, Cheng Soon Ong, Sören Sonnenburg, Gunnar Rätsch:
mGene.web: a web service for accurate computational gene finding. Nucleic Acids Res. 37(Web-Server-Issue): 312-316 (2009) - [c11]Alberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann:
Optimized expected information gain for nonlinear dynamical systems. ICML 2009: 97-104 - [c10]Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
Spanning Tree Approximations for Conditional Random Fields. AISTATS 2009: 408-415 - 2008
- [j4]Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch:
Support Vector Machines and Kernels for Computational Biology. PLoS Comput. Biol. 4(10) (2008) - [c9]Cheng Soon Ong, Alexander Zien:
An Automated Combination of Kernels for Predicting Protein Subcellular Localization. WABI 2008: 186-197 - 2007
- [j3]Uta Schulze, Bettina Hepp, Cheng Soon Ong, Gunnar Rätsch:
PALMA: mRNA to genome alignments using large margin algorithms. Bioinform. 23(15): 1892-1900 (2007) - [j2]Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson:
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8: 2443-2466 (2007) - [c8]Alexander Zien, Cheng Soon Ong:
Multiclass multiple kernel learning. ICML 2007: 1191-1198 - 2006
- [c7]Gunnar Rätsch, Bettina Hepp, Uta Schulze, Cheng Soon Ong:
PALMA: Perfect Alignments using Large Margin Algorithms. German Conference on Bioinformatics 2006: 104-113 - 2005
- [j1]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Learning the Kernel with Hyperkernels. J. Mach. Learn. Res. 6: 1043-1071 (2005) - [c6]Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel:
Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56 - 2004
- [c5]Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola:
Learning with non-positive kernels. ICML 2004 - 2003
- [c4]Cheng Soon Ong, Alexander J. Smola:
Machine Learning with Hyperkernels. ICML 2003: 568-575 - 2002
- [c3]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Hyperkernels. NIPS 2002: 478-485 - 2000
- [c2]Sock Yin Tai, Cheng Soon Ong, Noor Aida Abdullah:
On designing an automated Malaysian stemmer for the Malay language (poster session). IRAL 2000: 207-208
1990 – 1999
- 1999
- [c1]Leenesh Kumar Maisuria, Cheng Soon Ong, Wen Kin Lai:
A comparison of artificial neural networks and cluster analysis for typing biometrics authentication. IJCNN 1999: 3295-3299
Coauthor Index
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