Multi-Agent Planning, Learning, and Coordination Group (MapleCG)
Towards bridging the gaps between planning, learning, and coordination
LOW, BRYAN KIAN HSIANG |劉謙雄|
Associate Professor (with tenure) > CS > NUS
Director of AI Research > AI SG
Faculty Member > ISEP > NUS Graduate School (NGS)
Faculty Affiliate > Inst. of Data Science > NUS
Ph.D. > ECE ◊ RI > CMU


publications

RESEARCH SPOTLIGHTS     lay articles for light reading




ACCEPTED PAPERS & PREPRINTS
     Co-authors :         
  1. Fairness in federated learning.
    Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, Rachael Hwee Ling Sim, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang & Bryan Kian Hsiang Low.
    In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 8, pages 143-160, Academic Press, 2024.


  2. Federated sequential decision making: Bayesian optimization, reinforcement learning, and beyond.
    Zhongxiang Dai, Flint Xiaofeng Fan, Cheston Tan, Trong Nghia Hoang, Bryan Kian Hsiang Low & Patrick Jaillet.
    In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 14, pages 257-279, Academic Press, 2024.


  3. Data valuation in federated learning.
    Zhaoxuan Wu, Xinyi Xu, Rachael Hwee Ling Sim, Yao Shu, Xiaoqiang Lin, Lucas Agussurja, Zhongxiang Dai, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang & Bryan Kian Hsiang Low.
    In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 15, pages 281-296, Academic Press, 2024.


  4. Incentives in federated learning.
    Rachael Hwee Ling Sim, Sebastian Shenghong Tay, Xinyi Xu, Yehong Zhang, Zhaoxuan Wu, Xiaoqiang Lin, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang & Bryan Kian Hsiang Low.
    In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 16, pages 299-309, Academic Press, 2024.


  5. PINNACLE: PINN Adaptive ColLocation and Experimental points selection.
    Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng & Bryan Kian Hsiang Low.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    5% acceptance rate (spotlight)

  6. Robustifying and Boosting Training-Free Neural Architecture Search.
    Zhenfeng He, Yao Shu, Zhongxiang Dai & Bryan Kian Hsiang Low.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  7. Incentive-Aware Federated Learning with Training-Time Model Rewards.
    Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar & Bryan Kian Hsiang Low.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  8. Understanding Domain Generalization: A Noise Robustness Perspective.
    Rui Qiao & Bryan Kian Hsiang Low.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  9. A Unified Framework for Bayesian Optimization under Contextual Uncertainty.
    Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong & Bryan Kian Hsiang Low.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  10. Optimistic Bayesian Optimization with Unknown Constraints.
    Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  11. Leveraging Previous Tasks in Optimizing Risk Measures with Gaussian Processes.
    Quoc Phong Nguyen, Bryan Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 12th International Conference on Learning Representations (ICLR-24), Vienna, Austria, May 7 - 11, 2024.
    31% acceptance rate

  12. WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data.
    Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng & Bryan Kian Hsiang Low.
    arXiv:2310.00646, Oct 1, 2023.


  13. Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers.
    Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet & Bryan Kian Hsiang Low.
    arXiv:2310.02905, Oct 2, 2023.


  14. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks.
    Tiedong Liu & Bryan Kian Hsiang Low.
    arXiv:2305.14201, May 23, 2023.


  15. DeRDaVa: Deletion-Robust Data Valuation for Machine Learning.
    Xiao Tian, Rachael Hwee Ling Sim, Jue Fan & Bryan Kian Hsiang Low.
    In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, Feb 20 - Feb 27, 2024.
    23.75% acceptance rate

  16. Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates.
    Zhuanghua Liu, Luo Luo & Bryan Kian Hsiang Low.
    In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, Feb 20 - Feb 27, 2024.
    23.75% acceptance rate

  17. Decentralized Sum-of-Nonconvex Optimization.
    Zhuanghua Liu & Bryan Kian Hsiang Low.
    In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, Feb 20 - Feb 27, 2024.
    23.75% acceptance rate

  18. Quantum Bayesian Optimization.
    Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate

  19. Incentives in Private Collaborative Machine Learning.
    Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate

  20. Model Shapley: Equitable Model Valuation with Black-box Access.
    Xinyi Xu, Chi Thanh Lam, Chuan-Sheng Foo & Bryan Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate

  21. Bayesian Optimization with Cost-varying Variable Subsets.
    Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong & Bryan Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate

  22. Exploiting Correlated Auxiliary Feedback in Parameterized Bandits.
    Arun Verma, Zhongxiang Dai, Yao Shu & Bryan Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate

  23. Batch Bayesian Optimization For Replicable Experimental Design.
    Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 36: 37th Annual Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, LA, Dec 10 - Dec 16, 2023.
    26.1% acceptance rate


REFEREED PUBLICATIONS
         dblp
        Sorted by year 2K + 23 | 22 | 21 | 20 | 19 | 18 | 17 | 16 | 15 | 14 | 13 | 12 | 11 | 10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2
  1. Training-Free Neural Active Learning with Initialization-Robustness Guarantees.
    Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng & Bryan Kian Hsiang Low.
    In Proceedings of the 40th International Conference on Machine Learning (ICML-23), pages 12931-12971, Honolulu, HI, Jul 23 - 29, 2023.
    27.9% acceptance rate

  2. Collaborative Causal Inference with Fair Incentives.
    Rui Qiao, Xinyi Xu & Bryan Kian Hsiang Low.
    In Proceedings of the 40th International Conference on Machine Learning (ICML-23), pages 28300-28320, Honolulu, HI, Jul 23 - 29, 2023.
    27.9% acceptance rate

  3. Fair yet Asymptotically Equal Collaborative Learning.
    Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo & Bryan Kian Hsiang Low.
    In Proceedings of the 40th International Conference on Machine Learning (ICML-23), pages 21223-21259, Honolulu, HI, Jul 23 - 29, 2023.
    27.9% acceptance rate

  4. Pruning during Training by Network Efficacy Modeling.
    Mohit Rajpal, Yehong Zhang & Kian Hsiang Low.
    Machine Learning (Special Issue on ECML-PKDD 2022 Journal Track), volume 112, issue 7, pages 2653-2684, 2023.


  5. FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery.
    Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan-Sheng Foo & Kian Hsiang Low.
    In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS-23), pages 4033-4057, Valencia, Spain, Apr 25 - 27, 2023.
    29% acceptance rate

  6. No-Regret Sample-Efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities.
    Sebastian Tay, Quoc Phong Nguyen, Chuan-Sheng Foo & Kian Hsiang Low.
    In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS-23), pages 3591-3619, Valencia, Spain, Apr 25 - 27, 2023.
    29% acceptance rate

  7. Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-Linear Function Approximation.
    Chi Thanh Lam, Arun Verma, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 11th International Conference on Learning Representations (ICLR-23), Kigali, Rwanda, May 1 - 5, 2023.
    31.8% acceptance rate

  8. Federated Neural Bandits.
    Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 11th International Conference on Learning Representations (ICLR-23), Kigali, Rwanda, May 1 - 5, 2023.
    31.8% acceptance rate

  9. Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation.
    Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet & Kian Hsiang Low.
    In Proceedings of the 11th International Conference on Learning Representations (ICLR-23), Kigali, Rwanda, May 1 - 5, 2023.
    31.8% acceptance rate

  10. Recursive Reasoning-Based Training-Time Adversarial Machine Learning.
    Yizhou Chen, Zhongxiang Dai, Haibin Yu, Kian Hsiang Low & Teck-Hua Ho.
    Artificial Intelligence (Special Issue on Risk-Aware Autonomous Systems: Theory and Practice), volume 315, pages 103837, Feb 2023.


  11. Probably Approximate Shapley Fairness with Applications in Machine Learning.
    Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan-Sheng Foo & Kian Hsiang Low.
    In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), pages 5910-5918, Washington, DC, Feb 7 - Feb 14, 2023.
    19.6% acceptance rate (oral presentation)

  12. Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning.
    Quoc Phong Nguyen, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 35: 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22), pages 30542-30553, New Orleans, LA, Nov 28 - Dec 3, 2022.
    25.6% acceptance rate

  13. Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.
    Yao Shu, Zhongxiang Dai, Zhaoxuan Wu & Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 35: 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22), pages 33001-33015, New Orleans, LA, Nov 28 - Dec 3, 2022.
    25.6% acceptance rate

  14. Sample-Then-Optimize Batch Neural Thompson Sampling.
    Zhongxiang Dai, Yao Shu, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 35: 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22), pages 23331-23344, New Orleans, LA, Nov 28 - Dec 3, 2022.
    25.6% acceptance rate

  15. On Provably Robust Meta-Bayesian Optimization.
    Zhongxiang Dai, Yizhou Chen, Haibin Yu, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI-22), pages 475-485, Eindhoven, Netherlands, Aug 1-5, 2022.
    32.3% acceptance rate

  16. Neural Ensemble Search via Bayesian Sampling.
    Yao Shu, Yizhou Chen, Zhongxiang Dai & Kian Hsiang Low.
    In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI-22), pages 1803-1812, Eindhoven, Netherlands, Aug 1-5, 2022.
    32.3% acceptance rate

  17. On the Convergence of the Shapley Value in Parametric Bayesian Learning Games.
    Lucas Agussurja, Xinyi Xu & Kian Hsiang Low.
    In Proceedings of the 39th International Conference on Machine Learning (ICML-22), pages 180-196, Baltimore, MD, Jul 17-23, 2022.
    21.9% acceptance rate

  18. DAVINZ: Data Valuation using Deep Neural Networks at Initialization.
    Zhaoxuan Wu, Yao Shu & Kian Hsiang Low.
    In Proceedings of the 39th International Conference on Machine Learning (ICML-22), pages 24150-24176, Baltimore, MD, Jul 17-23, 2022.
    21.9% acceptance rate

  19. Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity.
    Sebastian Tay, Chuan-Sheng Foo, Urano Daisuke, Richalynn Leong & Kian Hsiang Low.
    In Proceedings of the 39th International Conference on Machine Learning (ICML-22), pages 21180-21204, Baltimore, MD, Jul 17-23, 2022.
    21.9% acceptance rate

  20. Bayesian Optimization under Stochastic Delayed Feedback.
    Arun Verma, Zhongxiang Dai & Kian Hsiang Low.
    In Proceedings of the 39th International Conference on Machine Learning (ICML-22), pages 22145-22167, Baltimore, MD, Jul 17-23, 2022.
    21.9% acceptance rate

  21. Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges.
    Rachael Hwee Ling Sim, Xinyi Xu & Kian Hsiang Low.
    In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-22), pages 5607-5614, Vienna, Austria, Jul 23-29, 2022.
    18.2% acceptance rate

  22. Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten.
    Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan & Kian Hsiang Low.
    In Proceedings of the 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS'22), pages 351-363, Nagasaki, Japan, May 30 - Jun 3, 2022.
    18.4% acceptance rate

  23. NASI: Label- and Data-agnostic Neural Architecture Search at Initialization.
    Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi & Kian Hsiang Low.
    In Proceedings of the 10th International Conference on Learning Representations (ICLR-22), Apr 25 - 29, 2022.
    32.29% acceptance rate

  24. Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning.
    Yizhou Chen, Shizhuo Zhang & Kian Hsiang Low.
    In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS-22), pages 9091-9113, Mar 28 - 30, 2022.
    29.2% acceptance rate

  25. Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards.
    Sebastian Tay, Xinyi Xu, Chuan-Sheng Foo & Kian Hsiang Low.
    In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-22), pages 9448-9456, Feb 22 - Mar 1, 2022.
    4.26% acceptance rate (oral presentation)

  26. Differentially Private Federated Bayesian Optimization with Distributed Exploration.
    Zhongxiang Dai, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), pages 9125-9139, Dec 6-14, 2021.
    25.6% acceptance rate

  27. Optimizing Conditional Value-At-Risk of Black-Box Functions.
    Quoc Phong Nguyen, Zhongxiang Dai, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), pages 4170-4180, Dec 6-14, 2021.
    25.6% acceptance rate

  28. Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee.
    Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan & Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), pages 1007-1021, Dec 6-14, 2021.
    25.6% acceptance rate

  29. Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning.
    Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan-Sheng Foo & Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), pages 16104-16117, Dec 6-14, 2021.
    25.6% acceptance rate

  30. Validation Free and Replication Robust Volume-based Data Valuation.
    Xinyi Xu, Zhaoxuan Wu, Chuan-Sheng Foo & Kian Hsiang Low.
    In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), pages 10837-10848, Dec 6-14, 2021.
    25.6% acceptance rate

  31. Learning to Learn with Gaussian Processes.
    Quoc Phong Nguyen, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21), pages 1466-1475, Jul 27-30, 2021.
    26.5% acceptance rate

  32. Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization.
    Quoc Phong Nguyen, Zhaoxuan Wu, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21), pages 1486-1495, Jul 27-30, 2021.
    26.5% acceptance rate

  33. Collaborative Bayesian Optimization with Fair Regret.
    Rachael Hwee Ling Sim, Yehong Zhang, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 9691-9701, Jul 18-24, 2021.
    21.5% acceptance rate

  34. Model Fusion for Personalized Learning.
    Chi Thanh Lam, Trong Nghia Hoang, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 5948-5958, Jul 18-24, 2021.
    21.5% acceptance rate

  35. Value-at-Risk Optimization with Gaussian Processes.
    Quoc Phong Nguyen, Zhongxiang Dai, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 38th International Conference on Machine Learning (ICML-21), pages 8063-8072, Jul 18-24, 2021.
    21.5% acceptance rate

  36. AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings.
    Trong Nghia Hoang, Shenda Hong, Cao Xiao, Kian Hsiang Low & Jimeng Sun.
    In Proceedings of the 30th The Web Conference (WWW'21), pages 3569–3581, Apr 19-23, 2021.
    20.6% acceptance rate

  37. Top-k Ranking Bayesian Optimization.
    Quoc Phong Nguyen, Sebastian Tay, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21), pages 9135-9143, Feb 2-9, 2021.
    21.4% acceptance rate

  38. An Information-Theoretic Framework for Unifying Active Learning Problems.
    Quoc Phong Nguyen, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21), pages 9126-9134, Feb 2-9, 2021.
    21.4% acceptance rate

  39. Convolutional Normalizing Flows for Deep Gaussian Processes.
    Haibin Yu, Dapeng Liu, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the International Joint Conference on Neural Networks (IJCNN'21), Jul 18-22, 2021.


  40. Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization.
    Sreejith Balakrishnan, Quoc Phong Nguyen, Kian Hsiang Low & Harold Soh.
    In Advances in Neural Information Processing Systems 33: 34th Annual Conference on Neural Information Processing Systems (NeurIPS'20), pages 4187-4198, Dec 6-12, 2020.
    20.1% acceptance rate

  41. Federated Bayesian Optimization via Thompson Sampling.
    Zhongxiang Dai, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 33: 34th Annual Conference on Neural Information Processing Systems (NeurIPS'20), pages 9687-9699, Dec 6-12, 2020.
    20.1% acceptance rate

  42. Variational Bayesian Unlearning.
    Quoc Phong Nguyen, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 33: 34th Annual Conference on Neural Information Processing Systems (NeurIPS'20), pages 16025-16036, Dec 6-12, 2020.
    20.1% acceptance rate

  43. Collaborative Machine Learning with Incentive-Aware Model Rewards.
    Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan & Kian Hsiang Low.
    In Proceedings of the 37th International Conference on Machine Learning (ICML-20), pages 8927-8936, Jun 12-18, 2020.
    21.8% acceptance rate

  44. R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games.
    Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet & Teck-Hua Ho.
    In Proceedings of the 37th International Conference on Machine Learning (ICML-20), pages 2291-2301, Jun 12-18, 2020.
    21.8% acceptance rate

  45. Private Outsourced Bayesian Optimization.
    Dmitrii Kharkovskii, Zhongxiang Dai & Kian Hsiang Low.
    In Proceedings of the 37th International Conference on Machine Learning (ICML-20), pages 5231-5242, Jun 12-18, 2020.
    21.8% acceptance rate

  46. Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion.
    Trong Nghia Hoang, Chi Thanh Lam, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 37th International Conference on Machine Learning (ICML-20), pages 4282-4292, Jun 12-18, 2020.
    21.8% acceptance rate

  47. Nonmyopic Gaussian Process Optimization with Macro-Actions.
    Dmitrii Kharkovskii, Chun Kai Ling & Kian Hsiang Low.
    In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS-20), pages 4593-4604, Aug 26-28, 2020.
    28.7% acceptance rate

  48. Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression.
    Tong Teng, Jie Chen, Yehong Zhang & Kian Hsiang Low.
    In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20), pages 5997-6004, New York, NY, Feb 7-12, 2020.
    20.6% acceptance rate

  49. Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception.
    Ruofei Ouyang & Kian Hsiang Low.
    Autonomous Robots (Special Issue on Multi-Robot and Multi-Agent Systems), volume 44, issue 3, pages 359-376, Mar 2020.
    Extended version of our AAAI-18 paper

  50. FCM-Sketch: Generic Network Measurements with Data Plane Support.
    Cha Hwan Song, Pravein Govindan Kannan, Kian Hsiang Low & Mun Choon Chan.
    In Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies (CoNEXT'20), pages 78-92, Dec 1-4, 2020.
    24% acceptance rate

  51. Implicit Posterior Variational Inference for Deep Gaussian Processes.
    Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low & Patrick Jaillet.
    In Advances in Neural Information Processing Systems 32: 33rd Annual Conference on Neural Information Processing Systems (NeurIPS'19), pages 14475-14486, Vancouver, Canada, Dec 7-12, 2019.
    3% acceptance rate (spotlight presentation)

  52. Bayesian Optimization with Binary Auxiliary Information.
    Yehong Zhang, Zhongxiang Dai & Kian Hsiang Low.
    In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI-19), pages 1222-1232, Tel Aviv, Israel, Jul 22-25, 2019.
    26.2% acceptance rate (plenary talk)
    Subsumes our work on Information-Based Multi-Fidelity Bayesian Optimization presented in NeurIPS'17 Workshop on Bayesian Optimization, Long Beach, CA, Dec 9, 2017.

  53. Bayesian Optimization Meets Bayesian Optimal Stopping.
    Zhongxiang Dai, Haibin Yu, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 1496-1506, Long Beach, CA, Jun 9-15, 2019.
    22.6% acceptance rate

  54. Collective Model Fusion for Multiple Black-Box Experts.
    Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low & Carleton Kingsford.
    In Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 2742-2750, Long Beach, CA, Jun 9-15, 2019.
    22.6% acceptance rate

  55. Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems.
    Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low & Jonathan P. How.
    In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), pages 7850-7857, Honolulu, HI, Jan 27-Feb 1, 2019.
    16.2% acceptance rate (oral presentation)

  56. Towards Robust ResNet: A Small Step but a Giant Leap.
    Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), pages 4285-4291, Macao, Aug 10-16, 2019.
    17.9% acceptance rate

  57. GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection.
    Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low & Mun Choon Chan.
    In Proceedings of the IEEE Conference on Communications and Network Security (CNS'19), pages 91-99, Washington, DC, Jun 10-12, 2019.
    27.8% acceptance rate

  58. Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression.
    Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the International Joint Conference on Neural Networks (IJCNN'19), Budapest, Hungary, Jul 14-19, 2019.
    52.4% acceptance rate

  59. Decentralized High-Dimensional Bayesian Optimization with Factor Graphs.
    Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang & Kian Hsiang Low.
    In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), pages 3231-3238, New Orleans, LA, Feb 2-8, 2018.
    24.55% acceptance rate

  60. Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception.
    Ruofei Ouyang & Kian Hsiang Low.
    In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), pages 3876-3883, New Orleans, LA, Feb 2-8, 2018.
    24.55% acceptance rate

  61. Artificial Intelligence Research in Singapore: Assisting the Development of a Smart Nation.
    Pradeep Varakantham, Bo An, Bryan Low & Jie Zhang.
    AI Magazine, volume 38, issue 3, pages 102-105, Fall 2017.


  62. Distributed Batch Gaussian Process Optimization.
    Erik Daxberger & Kian Hsiang Low.
    In Proceedings of the 34th International Conference on Machine Learning (ICML-17), pages 951-960, Sydney, Australia, Aug 6-11, 2017.
    25.9% acceptance rate

  63. A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression.
    Quang Minh Hoang, Trong Nghia Hoang & Kian Hsiang Low.
    In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), pages 2007-2014, San Francisco, CA, Feb 4-9, 2017.
    24.6% acceptance rate (oral presentation)

  64. A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models.
    Trong Nghia Hoang, Quang Minh Hoang & Kian Hsiang Low.
    In Proceedings of the 33rd International Conference on Machine Learning (ICML-16), pages 382-391, New York City, NY, Jun 19-24, 2016.
    24.3% acceptance rate

  65. Near-Optimal Active Learning of Multi-Output Gaussian Processes.
    Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pages 2351-2357, Phoenix, AZ, Feb 12-17, 2016.
    25.75% acceptance rate

  66. Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond.
    Chun Kai Ling, Kian Hsiang Low & Patrick Jaillet.
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pages 1860-1866, Phoenix, AZ, Feb 12-17, 2016.
    25.75% acceptance rate

  67. DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks.
    Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low & Tat-Seng Chua.
    In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pages 1469-1475, New York City, NY, Jul 9-15, 2016.
    <25% acceptance rate

  68. Multi-Agent Continuous Transportation with Online Balanced Partitioning.
    Chao Wang, Somchaya Liemhetcharat & Kian Hsiang Low.
    In Proceedings of the 15th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-16), pages 1303-1304, Singapore, May 9-13, 2016.


  69. Concept-based Hybrid Fusion of Multimodal Event Signals.
    Yuhui Wang, Christian von der Weth, Yehong Zhang, Kian Hsiang Low, Vivek Singh & Mohan Kankanhalli.
    In Proceedings of the IEEE International Symposium on Multimedia (ISM'16), pages 14-19, San Jose, CA, Dec 11-13, 2016.
    26.1% acceptance rate

  70. Inverse Reinforcement Learning with Locally Consistent Reward Functions.
    Quoc Phong Nguyen, Kian Hsiang Low & Patrick Jaillet.
    In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett, editors, Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems (NeurIPS'15), pages 1747-1755, Curran Associates, Inc., Montreal, Canada, Dec 7-12, 2015.
    21.9% acceptance rate

  71. Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems.
    Jie Chen, Kian Hsiang Low, Patrick Jaillet & Yujian Yao.
    IEEE Transactions on Automation Science and Engineering (Special Issue on Networked Cooperative Autonomous Systems), volume 12, issue 3, pages 901-921, Jul 2015.
    Extended version of our UAI-12 and RSS-13 papers

  72. A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data.
    Trong Nghia Hoang, Quang Minh Hoang & Kian Hsiang Low.
    In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 569-578, Lille, France, Jul 6-11, 2015.
    26.0% acceptance rate

  73. Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation.
    Kian Hsiang Low, Jiangbo Yu, Jie Chen & Patrick Jaillet.
    In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 2821-2827, Austin, TX, Jan 25-29, 2015.
    26.67% acceptance rate

  74. Recent Advances in Scaling up Gaussian Process Predictive Models for Large Spatiotemporal Data.
    Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu & Patrick Jaillet.
    In S. Ravela, A. Sandu, editors, Dynamic Data-Driven Environmental Systems Science - First International Conference, DyDESS'14, LNCS 8964, pages 167-181, Springer International Publishing, MIT, Cambridge, MA, Nov 5-7, 2014.
    Oral presentation

  75. Multi-Agent Ad Hoc Team Partitioning by Observing and Modeling Single-Agent Performance.
    Etkin Baris Ozgul, Somchaya Liemhetcharat & Kian Hsiang Low.
    In Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC'14), pages 1-7, Siem Reap, city of Angkor Wat, Cambodia, Dec 9-12, 2014.


  76. Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance.
    Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC'14) (Invited Paper to Special Session on Smart Cameras for Smart Environments), pages 115-120, Venezia, Italy, Nov 4-7, 2014.


  77. Active Learning is Planning: Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes.
    Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet and Mohan Kankanhalli.
    In T. Calders, F. Esposito, E. Hüllermeier, R. Meo, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD-14 Nectar (New Scientific and Technical Advances in Research) Track, Part III, LNCS 8726, pages 494-498, Springer Berlin Heidelberg, Nancy, France, Sep 15-19, 2014.


  78. Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization.
    Kian Hsiang Low, Nuo Xu, Jie Chen, Keng Kiat Lim & Etkin Baris Ozgul.
    In T. Calders, F. Esposito, E. Hüllermeier, R. Meo, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD-14 Nectar (New Scientific and Technical Advances in Research) Track, Part III, LNCS 8726, pages 499-503, Springer Berlin Heidelberg, Nancy, France, Sep 15-19, 2014.


  79. No One is Left "Unwatched": Fairness in Observation of Crowds of Mobile Targets in Active Camera Surveillance.
    Prabhu Natarajan, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI-14), including Prestigious Applications of Intelligent Systems (PAIS-14), pages 1155-1160, Prague, Czech Republic, Aug 18-22, 2014.


  80. GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model.
    Nuo Xu, Kian Hsiang Low, Jie Chen, Keng Kiat Lim & Etkin Baris Ozgul.
    In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14), pages 2585-2592, Quebec City, Canada, Jul 27-31, 2014.
    16.6% acceptance rate (oral presentation)
    Also appeared in RSS-14 Workshop on Non-Parametric Learning in Robotics, Berkeley, CA, Jul 12, 2014.

  81. Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes.
    Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet and Mohan Kankanhalli.
    In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 739-747, Beijing, China, Jun 21-26, 2014.
    22.4% acceptance rate (cycle 2)
    Also appeared in RSS-14 Workshop on Non-Parametric Learning in Robotics, Berkeley, CA, Jul 12, 2014.

  82. Multi-Robot Active Sensing of Non-Stationary Gaussian Process-Based Environmental Phenomena.
    Ruofei Ouyang, Kian Hsiang Low, Jie Chen & Patrick Jaillet.
    In Proceedings of the 13th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-14), pages 573-580, Paris, France, May 5-9, 2014.
    23.8% acceptance rate
    Also appeared in RSS-14 Workshop on Non-Parametric Learning in Robotics, Berkeley, CA, Jul 12, 2014.

  83. Decision-Theoretic Approach to Maximizing Fairness in Multi-Target Observation in Multi-Camera Surveillance.
    Prabhu Natarajan, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 13th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-14), pages 1521-1522, Paris, France, May 5-9, 2014.


  84. Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents.
    Trong Nghia Hoang & Kian Hsiang Low.
    In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 2298-2305, Beijing, China, Aug 3-9, 2013.
    13.2% acceptance rate (oral presentation)

  85. A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior.
    Trong Nghia Hoang & Kian Hsiang Low.
    In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 1394-1400, Beijing, China, Aug 3-9, 2013.
    28.0% acceptance rate

  86. Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations.
    Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan & Patrick Jaillet.
    In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13), pages 152-161, Bellevue, WA, Jul 11-15, 2013.
    31.3% acceptance rate

  87. Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System.
    Jie Chen, Kian Hsiang Low & Colin Keng-Yan Tan.
    In Proceedings of the Robotics: Science and Systems Conference (RSS-13), Berlin, Germany, Jun 24-28, 2013.
    30.1% acceptance rate

  88. Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms.
    Nannan Cao, Kian Hsiang Low & John M. Dolan.
    In Proceedings of the 12th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-13), pages 7-14, Saint Paul, MN, May 6-10, 2013.
    22.9% acceptance rate

  89. Adaptive Sampling of Time Series with Application to Remote Exploration.
    David R. Thompson, Nathalie Cabrol, Michael Furlong, Craig Hardgrove, Kian Hsiang Low, Jeffrey Moersch & David Wettergreen.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'13), pages 3463-3468, Karlsruhe, Germany, May 6-10, 2013.


  90. Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena.
    Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John M. Dolan & Gaurav S. Sukhatme.
    In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-12), pages 163-173, Catalina Island, CA, Aug 15-17, 2012.
    31.6% acceptance rate
    Also appeared in AAMAS-12 Workshop on Agents in Traffic and Transportation (ATT-12), Valencia, Spain, June 4-8, 2012.

  91. Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents.
    Jiangbo Yu, Kian Hsiang Low, Ali Oran & Patrick Jaillet.
    In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'12) (Invited Paper to Special Session on Large-Scale Application-Focused Multi-Agent Systems), pages 478-485, Macau, Dec 4-7, 2012.


  92. Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments.
    Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 6th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC'12), pages 1-6, Hong Kong, Oct 30 - Nov 2, 2012.


  93. Decentralized Active Robotic Exploration and Mapping for Probabilistic Field Classification in Environmental Sensing.
    Kian Hsiang Low, Jie Chen, John M. Dolan, Steve Chien & David R. Thompson.
    In Proceedings of the 11th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-12), pages 105-112, Valencia, Spain, June 4-8, 2012.
    20.4% acceptance rate
    Also appeared in IROS'11 Workshop on Robotics for Environmental Monitoring (WREM-11), San Francisco, CA, Sep 30, 2011.

  94. Decision-Theoretic Approach to Maximizing Observation of Multiple Targets in Multi-Camera Surveillance.
    Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low & Mohan Kankanhalli.
    In Proceedings of the 11th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-12), pages 155-162, Valencia, Spain, June 4-8, 2012.
    20.4% acceptance rate

  95. Intention-Aware Planning under Uncertainty for Interacting with Self-Interested, Boundedly Rational Agents.
    Trong Nghia Hoang & Kian Hsiang Low.
    In Proceedings of the 11th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-12), pages 1233-1234, Valencia, Spain, June 4-8, 2012.


  96. Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing.
    Kian Hsiang Low, John M. Dolan & Pradeep Khosla.
    In Proceedings of the 10th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-11), pages 753-760, Taipei, Taiwan, May 2-6, 2011.
    22.1% acceptance rate

  97. Autonomous Personal Vehicle for the First- and Last-Mile Transportation Services.
    Zhuang Jie Chong, Baoxing Qin, Tirthankar Bandyopadhyay, Tichakorn Wongpiromsarn, Edward Samuel Rankin, Marcelo H. Ang, Jr., Emilio Frazzoli, Daniela Rus, David Hsu & Kian Hsiang Low.
    In Proceedings of the 5th IEEE International Conference on Cybernetics and Intelligent Systems and 5th IEEE International Conference on Robotics, Automation and Mechatronics (CIS-RAM'11), pages 253-260, Qingdao, China, Sep 17-19, 2011.

    Also appeared in IROS'11 Workshop on Perception and Navigation for Autonomous Vehicles in Human Environment, San Francisco, CA, Sep 30, 2011.

  98. Telesupervised Remote Surface Water Quality Sensing.
    Gregg Podnar, John M. Dolan, Kian Hsiang Low & Alberto Elfes.
    In Proceedings of the IEEE Aerospace Conference, Big Sky, MT, Mar 6-13, 2010.


  99. Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing.
    Kian Hsiang Low, John M. Dolan & Pradeep Khosla.
    In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), pages 233-240, Thessaloniki, Greece, Sep 19-23, 2009.
    33.9% acceptance rate
    Also appeared in IPSN-09 Workshop on Sensor Networks for Earth and Space Science Applications (ESSA-09), San Francisco, CA, Apr 16, 2009.
    Also orally presented in RSS-09 Workshop on Aquatic Robots and Ocean Sampling, Seattle, WA, Jun 29, 2009.

  100. Cooperative Aquatic Sensing using the Telesupervised Adaptive Ocean Sensor Fleet.
    John M. Dolan, Gregg W. Podnar, Stephen Stancliff, Kian Hsiang Low, Alberto Elfes, John Higinbotham, Jeffrey C. Hosler, Tiffany A. Moisan & John Moisan.
    In Proceedings of the SPIE Conference on Remote Sensing of the Ocean, Sea Ice, and Large Water Regions, volume 7473, Berlin, Germany, Aug 31 - Sep 3, 2009.


  101. Robot Boats as a Mobile Aquatic Sensor Network.
    Kian Hsiang Low, Gregg Podnar, Stephen Stancliff, John M. Dolan & Alberto Elfes.
    In Proceedings of the IPSN-09 Workshop on Sensor Networks for Earth and Space Science Applications (ESSA-09), San Francisco, CA, Apr 16, 2009.


  102. Adaptive Multi-Robot Wide-Area Exploration And Mapping.
    Kian Hsiang Low, John M. Dolan & Pradeep Khosla.
    In Proceedings of the 7th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-08), pages 23-30, Estoril, Portugal, May 12-16, 2008.
    22.2% acceptance rate
    Also presented as a poster in RSS-09 Workshop on Aquatic Robots and Ocean Sampling, Seattle, WA, Jun 29, 2009.

  103. Adaptive Sampling for Multi-Robot Wide-Area Exploration.
    Kian Hsiang Low, Geoffrey J. Gordon, John M. Dolan & Pradeep Khosla.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'07), pages 755-760, Rome, Italy, Apr 10-14, 2007.


  104. Autonomic Mobile Sensor Network with Self-Coordinated Task Allocation and Execution.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews (Special Issue on Engineering Autonomic Systems), volume 36, issue 3, pages 315-327, May 2006.
    Extended version of our IJCAI-03, ICRA'04, and AAAI-04 papers
    Andrew P. Sage Best Transactions Paper Award for the best paper published in IEEE Trans. SMC - Part A, B, and C in 2006

  105. An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    Neural Computation, volume 17, issue 6, pages 1411-1445, Jun 2005.
    Extended version of our AAMAS-02, ICRA'03, and IJCAI-03 papers

  106. Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 19th National Conference on Artificial Intelligence (AAAI-04), pages 28-33, San Jose, CA, Jul 25-29, 2004.
    26.7% acceptance rate

  107. Reactive, Distributed Layered Architecture for Resource-Bounded Multi-Robot Cooperation: Application to Mobile Sensor Network Coverage.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), pages 3747-3752, New Orleans, LA, Apr 26 - May 1, 2004.


  108. Continuous-Spaced Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC'04) (Invited Paper to Special Session on Visual Surveillance), pages 198-203, Taipei, Taiwan, Mar 21-23, 2004.


  109. Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pages 1505-1506, Acapulco, Mexico, Aug 9-15, 2003.
    27.6% acceptance rate

  110. Action Selection in Continuous State and Action Spaces by Cooperation and Competition of Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 2nd International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS-03), pages 1056-1057, Melbourne, Australia, Jul 14-18, 2003.


  111. Enhancing the Reactive Capabilities of Integrated Planning and Control with Cooperative Extended Kohonen Maps.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'03), pages 3428-3433, Taipei, Taiwan, May 12-17, 2003.


  112. A Hybrid Mobile Robot Architecture with Integrated Planning and Control.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the 1st International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS-02), pages 219-226, Bologna, Italy, Jul 15-19, 2002.
    26% acceptance rate

  113. Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network.
    Kian Hsiang Low, Wee Kheng Leow & Marcelo H. Ang, Jr.
    In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'02), pages 3870-3875, Washington, DC, May 11-15, 2002.



TECHNICAL REPORTS
  1. Understanding and Improving Neural Architecture Search.
    Yao Shu.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Jan 2022.


  2. Exploiting Gradient Information for Modern Machine Learning Problems.
    Yizhou Chen.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Jan 2022.


  3. Sample-Efficient Automated Machine Learning with Bayesian Optimization.
    Zhongxiang Dai.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Jul 2021.


  4. Automated Machine Learning: New Advances on Bayesian Optimization.
    Dmitrii Kharkovskii.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Dec 2020.


  5. New Advances in Bayesian Inference for Gaussian Process and Deep Gaussian Process Models.
    Haibin Yu.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, May 2020.


  6. Data-Efficient Machine Learning with Multiple Output Types and High Input Dimensions.
    Yehong Zhang.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Dec 2017.


  7. Exploiting Decentralized Multi-Agent Coordination for Large-Scale Machine Learning Problems.
    Ruofei Ouyang.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Dec 2016.


  8. New Advances on Bayesian and Decision-Theoretic Approaches for Interactive Machine Learning.
    Trong Nghia Hoang.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Feb 2015.


  9. Gaussian Process-Based Decentralized Data Fusion and Active Sensing Agents: Towards Large-Scale Modeling and Prediction of Spatiotemporal Traffic Phenomena.
    Jie Chen.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Dec 2013.


  10. A Decision-Theoretic Approach for Controlling and Coordinating Multiple Active Cameras in Surveillance.
    Prabhu Natarajan.
    Ph.D. Thesis, Department of Computer Science, National University of Singapore, Dec 2013.


  11. Information-Theoretic Multi-Robot Path Planning.
    Nannan Cao.
    M.Sc. Thesis, Department of Computer Science, National University of Singapore, Sep 2012.


  12. Multi-Robot Adaptive Exploration and Mapping for Environmental Sensing Applications.
    Kian Hsiang Low.
    Ph.D. Thesis, Technical Report CMU-ECE-2009-024, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, Aug 2009.


  13. Adaptive Sampling for Multi-Robot Wide Area Prospecting.
    Kian Hsiang Low, Geoffrey J. Gordon, John M. Dolan, and Pradeep Khosla.
    In Technical Report CMU-RI-TR-05-51, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Oct 2005.


  14. Integrated Robot Planning and Control with Extended Kohonen Maps.
    Kian Hsiang Low.
    M.Sc. Thesis, Department of Computer Science, School of Computing, National University of Singapore, Jul 2002.
    Singapore Computer Society Prize for best M.Sc. Thesis 2002-2003

  15. Mobile Robots That Learn to Navigate.
    Kian Hsiang Low.
    Honors Thesis, Department of Computer Science, School of Computing, National University of Singapore, Apr 2001.


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