Selected Conference Publications

A74. K.S. Fong and M. Motani, “POVE: A Preoptimized Vault of Expressions for Symbolic Regression Research and Benchmarking”, KDD 2025, Toronto, ON, CA, Aug 2025. [Link]

A73. S. Wongso, R. Ghosh and M. Motani, “Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]

A72. K.S. Fong and M. Motani, “Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]

A71. K.S. Fong and M. Motani, “FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]

A70. J. Shi, K.S. Fong and M. Motani, “Analysis of Memory-Runtime Trade-offs in Caching Strategies for Genetic Programming Symbolic Regression”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]

A69. K.S. Fong and M. Motani, “SLIME: Supralocal Interpretable Model-Agnostic Explanations via Evolved Equation-Based Surrogates”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]

A68. Y. Leng, K.S. Fong and M. Motani, “Mutual Information-Based Evolutionary Feature Construction via Minimizing Redundancy and Maximizing Relevance”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]

A67. K.S. Fong and M. Motani, “Discovering Shared Function Structures with Adaptable Parameters for Multi-Level Modeling via Symbolic Regression”, GECCO 2025, Hot Off the Press Track, Malaga, Spain, Jul 2025. [Link]

A66. L.W. Chia and M. Motani, “OCC Is Better Than Terahertz Wave for 6G”, MobiSys 2025 (Poster), Anaheim, CA, US, Jun 2025. [Link]

A65. Y. Feng and Z. Chen, M. Motani, H. Yang, M. Wang, T.Q.S. Quek, “Remote Online Estimation of the Wiener Process: a Preprocessing Method to Ensure Distortion Convergence”, Ann Arbor, MI, US, Jun 2025. [Link] [Link]

A64. R. Ghosh and M. Motani, “Ordered V-information Growth: A New Perspective on Shared Information”, International Conference on Artificial Intelligence and Statistics (AISTATS), Phuket, Thailand, May 2025. [Link]

A63. K.S. 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

A62. 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, Jul 2024. [Link]

A61. K.S. Fong and M. Motani, “Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic Regression-Discovered Splits”, GECCO 2024, Hot Off the Press Track, Melbourne, Australia, Jul 2024. [Link]

A60. 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]

A59. 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]

A58. 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, Jun 2024. [Link] [Link]

A57. 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, Jun 2024. [Link] [Link]

A56. 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]

A55. 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]

A54. 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]

A53. 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]

A52. 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]

A51. K.S. Fong and M. Motani, “Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability”, GECCO 2023, Hot Off the Press Track, Lisbon, Portugal, Jul 2023. [Link]

A50. K.S. Fong and M. Motani, “DistilSR: A Distilled Version of Gene Expression Programming Symbolic Regression”, Genetic & Evolutionary Computation Conf. (GECCO), Lisbon, Portugal, Jul 2023. [Link]

A49. S. Wongso, R. Ghosh, and M. Motani, “Pointwise Sliced Mutual Information for Neural Network Explainability” ISIT 2023, Taipei, Taiwan, Jun 2023. [Link]

A48. 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]

A47. 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]

A46. R. Ghosh and M. Motani, “Local Intrinsic Dimensional Entropy”, AAAI Conference on Artificial Intelligence, Washington, DC, USA, Feb 2023. [Link] [Arxiv]

A45. 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]

A44. 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]

A43. 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]

A42. S. Wongso, R. Ghosh and M. Motani, “Understanding Deep Neural Networks Using Sliced Mutual Information”, IEEE ISIT 2022, Aalto, Finland, Jun 2022 [Link]

A41. 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]

A40. 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]

A39. 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]

A38. 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]

A37. J.C.M. Tan and M. Motani, “DropNet: Reducing Neural Network Complexity via Iterative Pruning”, International Conference on Machine Learning (ICML) 2020, Virtual Conference, Jul 2020. [Link]

A36. R. Bhat, R. Vaze, and M. Motani, “Age of Information Minimization in Fading Multiple Access Channels”, IEEE INFOCOM Age of Information Workshop, Virtual Conference, Jul 2020. [Link] [Link]

A35. C.T. Leung, R. Bhat, and M. Motani, “Multi-Label and Concatenated Neural Block Decoders”, IEEE ISIT 2020, Virtual Conference, Jun 2020. [Link]

A34. 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]

A33. 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]

A32. 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]

A31. 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.

A30. 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]

A29. 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]

A28. 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]

A27. 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]

A26. 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]

A25. 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]

A24. 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]

A23. C. Zhou, C.K. Tham and M. Motani, “Auction Meets Queuing: Information-Driven Data Purchasing in Stochastic Mobile Crowdsensing”, IEEE SECON 2017, San Diego, USA, Jun 2017. [Link]

A22. C. Zhou, C.K. Tham and M. Motani, “Optimizing Graphical Model Structure for Distributed Inference in Wireless Sensor Networks”, IEEE SECON 2016, London, UK, Jun 2016. [Link]

A21. N. Edalat, M. Motani, and J. Walrand, “Sizing and Control of Residential Solar Panel and Battery”, IEEE SmartGridComm 2015, Miami, FL, USA, Nov 2015.

A20. A. Tandon and M. Motani, “Has Green Energy Arrived? Delay Analysis for Energy Harvesting Communication Systems”, IEEE SECON 2014, Singapore, Singapore, Jun 2014.

A19. N. Edalat, M. Motani, L. Huang and J. Walrand, “Control of systems that store renewable energy”, ACM e-Energy 2014, Cambridge, UK, Jun 2014.

A18. S.R. Singh and M. Motani, “Demonstrating a Dynamic Multi-Channel Access 802.11 Mesh Network Prototype for High Bandwidth Requirements”, in ACM MobiCom 2010 (demo paper), Chicago, IL, USA, Sep 2010. [pdf]

A17. S.R. Singh and M. Motani, “Mesh Testbed for Multi-channel MAC Development: Design and Experimentation”, in ACM MobiCom 2010 Workshop on Wireless Network Testbeds, Experimental evaluation and Characterization, Chicago, IL, USA, Sep 2010. [pdf]

A16. S.R. Singh, B. de Silva, T. Luo and M. Motani, “Dynamic Spectrum Cognitive MAC (DySCO-MAC) for Wireless Mesh & Adhoc Networks”, in IEEE INFOCOM Workshop on Cognitive Wireless Communications and Networking, San Diego, California, USA, Mar 2010. [pdf]

A15. Y. Jin, M. Motani, W.S. Soh, and J. Zhang, “SparseTrack: Enhancing Indoor Pedestrian Tracking with Sparse Infrastructure Support”, in IEEE INFOCOM, San Diego, California, USA, Mar 2010. [pdf]

A14. A. Natarajan, B. de Silva, KK Yap and M. Motani, “Link Layer Behavior of Body Area Networks at 2.4 GHz”, in ACM MobiCom 2009, Beijing, China, Sep 2009. [pdf]

A13. T. Luo and M. Motani, “Cognitive DISH: Virtual Spectrum Sensing Meets Cooperation”, in SECON 2009, Rome, Italy, Jun 2009. [pdf]

A12. A. Natarajan, B. de Silva, K.K. Yap and M. Motani, “To Hop or Not to Hop: Network Architecture for Body Sensor Networks”, in SECON 2009, Rome, Italy, Jun 2009. [pdf]

A11. W. Wang, M. Motani and V. Srinivasan, “Dependent Link Padding Algorithms for Low Latency Mix Systems”, in ACM CCS 2008, Alexendria, VA, USA, Oct 2008. [pdf]

A10. T. Luo, M. Motani and V. Srinivasan, “Analyzing DISH for Multi-Channel MAC Protocols in Wireless Networks”, in ACM MobiHoc 2008, May 2008. [pdf]

A9. W. Wei, V. Srinivasan and M. Motani, “Adaptive Contact Probing Mechanisms for Delay Tolerant Applications”, in ACM MobiCom 2007, Montreal, Quebec, Canada, Sep 2007. [pdf]

A8. T. Luo, M. Motani and V. Srinivasan, “Altruistic cooperation for energy-efficient multi-channel MAC protocols”, in ACM MobiCom 2007, Montreal, Quebec, Canada, Sep 2007. [pdf]

A7. L. Ong and M. Motani, “Optimal Routing for Decode-and-Forward based Cooperation in Wireless Networks”, in IEEE SECON 2007, Jun 2007. [pdf]

A6. A. Natarajan, M. Motani and V. Srinivasan, “Understanding Urban Interactions from Bluetooth Phone Contact Traces”, in Passive & Active Measurement (PAM) 2007, Apr 2007. [pdf]

A5. V. Srinivasan, M. Motani, and W.T. Ooi, “Analysis and Implications of Contact Patterns Derived from Student Campus Schedules”, ACM MobiCom 2006, Los Angeles, CA, USA, Sep 2006. [pdf]

A4. K.K. Yap, W.L. Yeow, M. Motani, and C.K. Tham, “Simple directional antennas: Improving the performance of wireless multihop networks”, IEEE Infocom, Apr 2006. [pdf]

A3. K.K. Yap, V. Srinivasan, and M. Motani, “MAX: Human-centric search of the physical world”, ACM SenSys 2005, San Diego, CA, USA, Nov 2005. [pdf]

A2. M. Motani, V. Srinivasan, and P. Nuggehalli, “PeopleNet: Engineering a wireless virtual social network”, ACM MobiCom 2005, Cologne, Germany, Sep 2005. [pdf]

A1. M. Motani and H.K. Garg, “Instantaneous feedback in an interactive classroom”, In Proceedings of Int’l Conference on Engineering Education (Manchester, UK), Aug 2002. [pdf]