AI & Machine Learning Conference Papers

C56. KS Fong and M. Motani, “SyREC: A Symbolic-Regression-Based Ensemble Combiner”, IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), Herndon, VA, USA, Oct. 2024

C55. KJ Ng, RSJ Goh, KS Fong, DHT Tan, CT Leung, SH Ong, J. Tan, M. Motani, and MJR Lim, “Artificial Intelligence-based Agglomerative Clustering of Patient Phenotypes and Disease Factors in Spontaneous Intracerebral Hemorrhage”, 2024 Congress of Neurological Surgeons (CNS) Annual Meeting, Houston, Texas, USA, Sep. 2024. [Link]

C54. K.S. Fong and M. Motani, “MetaSR: A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression”, Genetic & Evolutionary Computation Conf. (GECCO), Melbourne, Australia, July 2024. [Link]

C53. K.S. Fong and M. Motani, “Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic Regression-Discovered Splits”, Hot Off the Press Track, GECCO 2024, Melbourne, Australia, July 2024. [Link]

C52. S. Wongso, C.T. Leung, R. Ghosh, and M. Motani, “V-Fair Classifier: Analyzing Adversarially Fair Classifier from V-Information Perspective”, IEEE ISIT 2024 Workshop on Information-Theoretic Methods for Trustworthy Machine Learning, Athens, Greece, Jul. 2024. [Link]

C51. K.S. Fong and M. Motani, “Explainable and Privacy-Preserving Machine Learning via Domain-Aware Symbolic Regression”, ACM Conference on Health, Inference, and Learning (ACM-CHIL 2024), New York, NY, Jun. 2024. [Link] [Link]

C50. K.S. Fong and M. Motani, “Symbolic Regression for Discovery of Medical Equations: A Case Study on Glomerular Filtration Rate Estimation Equations”, IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, June 2024. [Link] [Link]

C49. J.C.M. Tan and M. Motani, “Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge”, IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, June 2024. [Link] [Link]

C48. C.T. Leung, R. Ghosh, and M. Motani, “Multi-Task Generalizable Communication: Beyond the Information Bottleneck”, IEEE International Confrence on Communications (ICC), Denver, CO, USA, Jue 2024. [Link]

C47. K.S. Fong and M. Motani, “Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data” International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024. [Link]

C46. K.S. Fong and M. Motani, “Symbolic Regression Enhanced Decision Trees for Classification Tasks” Annual AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, Feb. 2024. [Link]

C45. E. Toh, B. Chek, S.H. Ong, P.I. Ngam, T.T. Yeo, V. Nga, M. Motani, M. Lim, “Predicting Clinically Significant Hematoma Expansion and Outcomes in Patients with Intracerebral Hemorrhage”, AI Health Summit, Singapore, Nov. 2023. [Link]

C44. J.C.M. Tan and M. Motani, “Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments”, IEEE International Conference on Development and Learning (ICDL), Macau, China, Nov. 2023. [Link] [Arxiv]

C43. E. Toh, B. Yan, I. Lim, D. Yap, W.J. Wee, K.J. Ng, V. Nga, T.T. Yeo, M. Motani, and M. Lim, “The Role of Intracranial Pressure Variability as a Predictor for Intracranial Hypertension and Mortality in Critically Ill Patients,” Brain and Spine, Vol. 3, Supp. 1, Abstracts of EANS2023, Sep 2023. [Link]

C42. D. Ho and M. Motani, “Multi-view Modelling of Longitudinal Health Data for Improved Prognostication of Colorectal Cancer Recurrence”, Machine Learning for Healthcare (MLHC), New York, NY, USA, Aug. 2023. [Link]

C41. K.S. Fong and M. Motani, “Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability”, Hot Off the Press Track, GECCO 2023, Lisbon, Portugal, July 2023. [Link]

C40. K.S. Fong and M. Motani, “DistilSR: A Distilled Version of Gene Expression Programming Symbolic Regression”, Genetic & Evolutionary Computation Conf. (GECCO), Lisbon, Portugal, July 2023. [Link]

C39. S. Wongso, R. Ghosh, and M. Motani, “Pointwise Sliced Mutual Information for Neural Network Explainability” ISIT 2023, Taipei, Taiwan, June 2023. [Link]

C38. C. Ming, G.J.W. Lee, Y.H. Teo, Y.N. Teo, E.M.S. Toh, T.Y.W. Li, C.Y. Guo, J. Ding, X. Zhou, H.L. Teoh, S.C. Seow, L.L.L. Yeo, C.H. Sia, M. Motani, and B.Y.Q. Tan, “Machine Learning Modelling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients”, 9th European Stroke Conference, Munich, Germany, May 2023. [Link]

C37. K.S. Fong, S. Wongso and M. Motani, “Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms”, International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. [Link]

C36. L.W Chia and M. Motani, “Generating synthetic data and training muzzle flash detection systems using GANs”, SPIE Defense + Commercial Sensing, Orlando, FL, USA, May 2023. [Link]

C35. L.W Chia and M. Motani, “Expandable SPAD-based real-time gun muzzle flash localization system using FPGAs and deep-learning”, SPIE Defense + Commercial Sensing, Orlando, FL, USA, May 2023. [Link]

C34. S. Wongso, R. Ghosh and M. Motani, “Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks”, International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, Apr. 2023. [Link]

C33. R. Ghosh and M. Motani, “Local Intrinsic Dimensional Entropy”, AAAI Conference on Artificial Intelligence, Washington, DC, USA, Feb. 2023. [Link] [Arxiv]

C32. J.C.M. Tan and M. Motani, “Using Hippocampal Replay to Consolidate Experiences in Memory-Augmented Reinforcement Learning”, Workshop on Memory in Artificial and Real Intelligence (MemARI) at NeurIPS 2022, New Orleans, USA, Dec. 2022. [Link]

C31. D. Ho and M. Motani, “Machine and Deep Learning methods for Predicting Immune Checkpoint Blockade Response”, Machine Learning for Health (ML4H 2022), New Orleans, USA, Nov. 2022. [Link]

C30. S. Gao and M. Motani, “Combining Blind Equalization and Automatic Modulation Classification in a Loop Structure”, IEEE Globecom 2022, Rio de Janeiro, Brazil, Dec. 2022. [Link]

C29. MJR Lim, RQHao Chong, KJ Ng, B. Tan, LLL Yeo, YL Low, B. Soon, WNH Loh, K. Teo, VDW Nga, TT Yeo, and M. Motani, “Prognostication of Outcomes and Surgical Intervention in Spontaneous Intraparenchymal Hemorrhage: A Propensity Score-matched Analysis with Support Vector Machine ”, 2022 Congress of Neurological Surgeons (CNS) Annual Meeting, San Francisco, CA, USA, Oct. 2022. [Link]

C28. V.V. Lee,  A.T.L. Truong, W. Thone, X.Z. Low, N. Le, S. Vijayakumar, N.Y. Lau, C.E. Chua, K.T.H. Siah, Mehul Motani, D. Ho, and  A. Blasiak, “It takes a village: engineering and behavioral research toward the development of a digital outcome measure for constipation management that patients and doctors want to use”, United European Gastroenterology (UEG Week 2022), Vienna, Austria, Oct. 2022. [Link]

C27. A. Li, M. L. Ong, C. W. Oei, W. Lian, H. P. Phua, L. H. Htet, W. Y. Lim, and M. Motani, “Unified Auto Clinical Scoring (Uni-ACS) with Interpretable ML models”, Machine Learning for Healthcare (MLHC 2022), Durham, NC, USA, Aug. 2022. [Link]

C26. S. Wongso, R. Ghosh and M. Motani, “Understanding Deep Neural Networks Using Sliced Mutual Information”, IEEE ISIT 2022, Aalto, Finland, June 2022 [Link]

C25. R. Ghosh and M. Motani, “Network-to-Network Regularization: Enforcing Occam’s Razor to Improve Generalization”, 35th Conf. on Neural Information Processing Systems (NeurIPS 2021), Dec. 2021. [Link]

C24. V. Malik, R. Ghosh and M. Motani, “Achieving Low Complexity Neural Decoders via Iterative Pruning”, Workshop on ML for Systems at NeurIPS 2021, Dec. 2021. [Link]

C23. D. Ho, I.B.H. Tan, and M. Motani, “Prognosticating Colorectal Cancer Recurrence using an Interpretable Deep Multi-view Network”, Machine Learning for Health 2021 (ML4H), Proceedings for Machine Learning Research (PMLR), Dec. 2021. [Link]

C22. D. Ho, I.B.H. Tan, and M. Motani, “Deep Multi-View Learning for Colorectal Cancer Prediction”, SingHealth Duke-NUS Scientific Congress 2021, Poster presentation, Sep. 2021.

C21. D. Ho, I.B.H. Tan, and M. Motani, “Predictive models for colorectal cancer recurrence using multi-modal healthcare data”, ACM Conference on Health, Inference, and Learning (ACM-CHIL 2021), Virtual Conference, Apr. 2021. [Link]

C20. D. Ho, DQ Chong, B. Tay , IB Tan, and M. Motani, “Prognosticating colorectal cancer recurrence using machine learning techniques”, 2020 IEEE International Conference on E-Health Networking, Application and Services (HEALTHCOM) [Link]

C19. J.C.M. Tan and M. Motani, “DropNet: Reducing Neural Network Complexity via Iterative Pruning”, International Conference on Machine Learning (ICML) 2020, Virtual Conference, July 2020. [Link]

C18. C.T. Leung, R. Bhat, and M. Motani, “Multi-Label and Concatenated Neural Block Decoders”, IEEE ISIT 2020, Virtual Conference, June 2020. [Link]

C17. S. Liu and M. Motani, “Exploring Unique Relevance for Mutual Information based Feature Selection”, IEEE ISIT 2020, Virtual Conference, June 2020. [Link]

C16. C.T. Leung, R. Bhat, and M. Motani, “Multi-Label Neural Decoders for Block Codes”, IEEE ICC 2020, Virtual Conference, June 2020.

C15. S. Liu and M. Motani, “Exploring Unique Relevance for Mutual Information based Feature Selection”, NeurIPS 2019 Workshop on Information Theory & Machine Learning (ITML), Vancouver, Canada, Dec. 2019. [Link]

C14. S. Liu and M. Motani, “Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks”, NeurIPS 2019 Workshop on Machine Learning for Health (ML4H), Vancouver, Canada, Dec. 2019. [Link]

C13. M.L. Ong, A. Li, and M. Motani, “Explainable and Actionable Machine Learning Models for Electronic Health Record Data”, 17th Int’l Conf on Biomedical Eng, Abstract Number: ICBME1309, Singapore, Dec. 2019. [Link]

C12. D. Ho, F.L. Leong, D.Q.Q. Chong, I.B.H. Tan, P. Krishnaswamy, and M. Motani, “Deep Learning Based Prediction of Colorectal Cancer Recurrence and Survival”, 17th Int’l Conf on Biomedical Eng, Abstract Number: ICBME1397, Singapore, Dec. 2019.

C11. C.T. Leung, R. Bhat, and M. Motani, “Low-Latency Neural Decoders for Linear and Non-Linear Block Codes”, IEEE GLOBECOM 2019, Hawaii, USA, Dec. 2019.

C10. S. Liu, J. Yao and M. Motani, “Early Prediction of Vital Signs Using Generative Boosting via LSTM Networks”, IEEE BIBM 2019, San Diego,CA, USA, Nov. 2019. [Link]

C9. R. Ghosh, A. Gupta, and M. Motani, “Investigating Convolutional Neural Networks using Spatial Orderness”, IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea, Oct. 2019. [Link]

C8. S. Liu, M.L. Ong, K.K. Mun, J. Yao and M. Motani, “Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling”, International Conference in Computing in Cardiology (CINC), Sep. 2019. [Link]

C7. J. Yao, M.L. Ong, K.K. Mun, S. Liu and M. Motani, “Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis”, International Conference in Computing in Cardiology (CINC), Sep. 2019. [Link]

C6. L. Zhou, V. Tan, and M. Motani, “Second-Order Asymptotically Optimal Statistical Classification”, IEEE ISIT 2019, Paris, France, July 2019.

C5. S. Liu and M. Motani, “Feature Selection Based on Unique Relevant Information for Health Data”, NeurIPS 2018 Workshop on Machine Learning for Health (ML4H), Montreal, Canada, Dec. 2018. [Link]

C4. Y. Jia and M. Motani, “Deep Spatio-Temporal Feature Learning using Autoencoders”, NeurIPS 2018 Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, Montreal, Canada, Dec. 2018. [Link]

C3. S. Liu, C. Zhou, Y. Jia, and M. Motani, “SURI: Feature Selection Based on Unique Relevant Information for Health Data”’, IEEE BIBM 2018, Madrid, Spain, Dec. 2018. [Link]

C2. J. Yao, C. Zhou, and M. Motani, “Spatio-Temporal Autoencoder for Feature Learning in Patient Data with Missing Observations”, IEEE BIBM 2017, Kansas City, MO, USA, Nov. 2017. [Link]

C1. C. Zhou, J. Yao, M. Motani, and J.W. Chew, “Learning Deep Representations from Heterogeneous Patient Data for Predictive Diagnosis” ACM BCB 2017, Boston, MA, USA, Aug. 2017. [Link]

AI & Machine Learning Journal Papers

J17. S. Liu and M. Motani, “Improving Mutual Information based Feature Selection by Boosting Unique Relevance”, Manuscript submitted for publication, 2024.

J16. S. Liu, R. Ghosh and M. Motani, “Towards Better Long-range Time Series Forecasting using Generative Forecasting”, Manuscript submitted for journal publication, 2024.

J15. C.T. Leung, R. Ghosh, and M. Motani, “Towards Robust Scale-Invariant Mutual Information Estimators”, Submitted for journal publication, 2024.

J14. K.J. Ng, R.S.J. Goh, S.S.H. Goh, L.L.L. Yeo, M. Motani, B.Y.Q. Tan, and M.J.R. Lim, “Prediction of Poor Functional Status Post Acute Ischemic Stroke: A Machine Learning Approach”, Manuscript submitted for journal publication, 2024.

J13. R. Ghosh and M. Motani, “Task-Aware Generalization Bounds using Generator Spaces”, Manuscript submitted for journal publication, 2023.

J12. C. Ming, G.J.W. Lee, Y.H. Teo, Y.N. Teo, E.M.S. Toh, T.Y.W. Li, C.Y. Guo, J. Ding, X. Zhou, H.L. Teoh, S.C. Seow, L.L.L. Yeo, C.H. Sia, M. Motani, and B.Y.Q. Tan, “Machine Learning Modelling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients” Journal of Personalized Medicine, accepted, May 2024.

J11. M. Lim, R. Quek, K.J. Ng, B. Tan, L. Yeo, Y.L. Low, B. Soon, W. Loh, K. Teo, V. Nga, T.T. Yeo and M. Motani, “Prognostication of Outcomes in Spontaneous Intracerebral Hemorrhage: A Propensity Score–Matched Analysis with Support Vector Machine”, World Neurosurgery, vol. 182, Feb. 2024. [Link]

J10. J. Sumner, A. Bundele, H. W. Lim, P. Phan, M. Motani, and A. Mukhopadhyay, “Developing an artificial intelligence-driven nudge intervention to improve medication adherence: A human-centred design approach”, Journal of Medical Systems, vol. 48, article 3, Nov. 2023 [Link]

J9. S. Liu, R. Ghosh and M. Motani, “AP: Selective Activation for De-sparsifying Pruned Networks”, Transactions on Machine Learning Research (TMLR), ISSN 2835-8856, Sep 2023. [Link]

J8. E.M.S. Toh, K.J. Ng, M. Motani, and M.J.R. Lim, “Letter: Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success from Video” Neurosurgery, 93(3), Sep 2023. [Link]

J7. S. Liu, R. Ghosh, J.C.M. Tan and M. Motani, “Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks”, Transactions on Machine Learning Research (TMLR), ISSN 2835-8856, Aug 2023. [Link]

J6. C.T. Leung, R. Bhat and M. Motani, “Low-Latency Energy-Efficient Neural Decoders for Block Codes”, IEEE Trans. Green Commun., vol. 7, issue 2, pp. 680-691, June 2023. [Link]

J5. E.M.S. Toh, B. Yan, I.C. Lim, D. Yap, W.J. Wee, K.J. Ng, V.D.W. Nga, M. Motani, and M.J.R. Lim, “The Role of Intracranial Pressure Variability as a Predictor for Intracranial Hypertension and Mortality in Critically Ill Patients”, Journal of Neurosurgery, May 2023. [Link]

J4. A. Mukhopadhyay, J. Sumner, L.H. Ling, R. Quek, A. Tan, G.G. Teng, S.K. Seetharaman, S.P.K. Gollamudi, D. Ho, M. Motani “Personalized dosing using the CURATE.AI algorithm: Protocol for a feasibility study in patients with hypertension and type II diabetes mellitus”, Int J Environ Res Public Health. 2022 Jul 23; 19(15): 8979. [Link]

J3. M. Lim, R. Quek, K.J. Ng, W. Loh, L. Sein, K. Teo, V. Nga, T.T. Yeo and M. Motani, “Machine Learning Prognosticates Functional Outcomes better than Clinical Scores in Spontaneous Intracerebral Haemorrhage”, Journal of Stroke and Cerebrovascular Diseases, Volume 31, Issue 2, Feb. 2022. [Link] [Link]

J2. L. Zhou, V. Tan and M. Motani, “Second-Order Asymptotically Optimal Statistical Classification”, Information and Inference: A Journal of the IMA, vol. 9, no. 1, pp 81-111, Mar. 2020. [Link]

J1. C. Zhou, J. Yao and M. Motani, “Optimizing Autoencoders for Learning Deep Representations from Health Data”, IEEE J. Biomed. Health Inform., vol. 23, no. 1, pp. 103-111, Jan. 2019. [Link]