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2020 – today
- 2023
[j7]Yizhou Chen, Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low
, Teck-Hua Ho:
Recursive reasoning-based training-time adversarial machine learning. Artif. Intell. 315: 103837 (2023)- [c95]Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low:
FedHQL: Federated Heterogeneous Q-Learning. AAMAS 2023: 2810-2812 - [i58]Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer:
FedHQL: Federated Heterogeneous Q-Learning. CoRR abs/2301.11135 (2023) - [i57]Tiedong Liu, Bryan Kian Hsiang Low:
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks. CoRR abs/2305.14201 (2023) - 2022
- [c94]Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. AAAI 2022: 9448-9456 - [c93]Yizhou Chen, Shizhuo Zhang, Bryan Kian Hsiang Low:
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. AISTATS 2022: 9091-9113 - [c92]Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan, Bryan Kian Hsiang Low:
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten. AsiaCCS 2022: 351-363 - [c91]Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low:
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. ICLR 2022 - [c90]Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low:
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. ICML 2022: 180-196 - [c89]Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low:
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. ICML 2022: 21180-21204 - [c88]Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low:
Bayesian Optimization under Stochastic Delayed Feedback. ICML 2022: 22145-22167 - [c87]Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low:
DAVINZ: Data Valuation using Deep Neural Networks at Initialization. ICML 2022: 24150-24176 - [c86]Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low:
Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges. IJCAI 2022: 5607-5614 - [c85]Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS 2022 - [c84]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. NeurIPS 2022 - [c83]Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low:
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. NeurIPS 2022 - [c82]Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On provably robust meta-Bayesian optimization. UAI 2022: 475-485 - [c81]Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low:
Neural ensemble search via Bayesian sampling. UAI 2022: 1803-1812 - [i56]Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low:
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. CoRR abs/2201.09785 (2022) - [i55]Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan, Bryan Kian Hsiang Low:
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten. CoRR abs/2202.13585 (2022) - [i54]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Rectified Max-Value Entropy Search for Bayesian Optimization. CoRR abs/2202.13597 (2022) - [i53]Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng:
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization. CoRR abs/2205.04901 (2022) - [i52]Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low:
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. CoRR abs/2205.07428 (2022) - [i51]Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandit. CoRR abs/2205.14309 (2022) - [i50]Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On Provably Robust Meta-Bayesian Optimization. CoRR abs/2206.06872 (2022) - [i49]Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low:
Bayesian Optimization under Stochastic Delayed Feedback. CoRR abs/2206.09341 (2022) - [i48]Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR abs/2210.06850 (2022) - [i47]Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Kian Hsiang Low:
Probably Approximate Shapley Fairness with Applications in Machine Learning. CoRR abs/2212.00630 (2022) - 2021
- [c80]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
An Information-Theoretic Framework for Unifying Active Learning Problems. AAAI 2021: 9126-9134 - [c79]Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet:
Top-k Ranking Bayesian Optimization. AAAI 2021: 9135-9143 - [c78]Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Model Fusion for Personalized Learning. ICML 2021: 5948-5958 - [c77]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Value-at-Risk Optimization with Gaussian Processes. ICML 2021: 8063-8072 - [c76]Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet:
Collaborative Bayesian Optimization with Fair Regret. ICML 2021: 9691-9701 - [c75]Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet:
Convolutional Normalizing Flows for Deep Gaussian Processes. IJCNN 2021: 1-6 - [c74]Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low:
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. NeurIPS 2021: 1007-1021 - [c73]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Optimizing Conditional Value-At-Risk of Black-Box Functions. NeurIPS 2021: 4170-4180 - [c72]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS 2021: 9125-9139 - [c71]Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Validation Free and Replication Robust Volume-based Data Valuation. NeurIPS 2021: 10837-10848 - [c70]Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. NeurIPS 2021: 16104-16117 - [c69]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning to learn with Gaussian processes. UAI 2021: 1466-1475 - [c68]Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-maximizers entropy search for efficient Bayesian optimization. UAI 2021: 1486-1495 - [i46]Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet:
Convolutional Normalizing Flows for Deep Gaussian Processes. CoRR abs/2104.08472 (2021) - [i45]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Value-at-Risk Optimization with Gaussian Processes. CoRR abs/2105.06126 (2021) - [i44]Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. CoRR abs/2107.14465 (2021) - [i43]Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low:
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. CoRR abs/2109.00817 (2021) - [i42]Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low:
Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search. CoRR abs/2109.02533 (2021) - [i41]Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low:
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. CoRR abs/2110.14074 (2021) - [i40]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR abs/2110.14153 (2021) - [i39]Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. CoRR abs/2112.09327 (2021) - 2020
- [j6]Ruofei Ouyang, Bryan Kian Hsiang Low:
Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception. Auton. Robots 44(3-4): 359-376 (2020) - [c67]Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low:
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. AAAI 2020: 5997-6004 - [c66]Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low:
Nonmyopic Gaussian Process Optimization with Macro-Actions. AISTATS 2020: 4593-4604 - [c65]Cha Hwan Song, Pravein Govindan Kannan, Bryan Kian Hsiang Low, Mun Choon Chan:
FCM-sketch: generic network measurements with data plane support. CoNEXT 2020: 78-92 - [c64]Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho:
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. ICML 2020: 2291-2301 - [c63]Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. ICML 2020: 4282-4292 - [c62]Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low:
Private Outsourced Bayesian Optimization. ICML 2020: 5231-5242 - [c61]Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low:
Collaborative Machine Learning with Incentive-Aware Model Rewards. ICML 2020: 8927-8936 - [c60]Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh:
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. NeurIPS 2020 - [c59]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. NeurIPS 2020 - [c58]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. NeurIPS 2020 - [i38]Dmitrii Kharkovskii, Chun Kai Ling, Kian Hsiang Low:
Nonmyopic Gaussian Process Optimization with Macro-Actions. CoRR abs/2002.09670 (2020) - [i37]Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho:
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. CoRR abs/2006.16679 (2020) - [i36]Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. CoRR abs/2010.10154 (2020) - [i35]Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low:
Collaborative Machine Learning with Incentive-Aware Model Rewards. CoRR abs/2010.12797 (2020) - [i34]Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low:
Private Outsourced Bayesian Optimization. CoRR abs/2010.12799 (2020) - [i33]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. CoRR abs/2010.12883 (2020) - [i32]Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh:
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. CoRR abs/2011.08541 (2020) - [i31]Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet:
Top-k Ranking Bayesian Optimization. CoRR abs/2012.10688 (2020) - [i30]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
An Information-Theoretic Framework for Unifying Active Learning Problems. CoRR abs/2012.10695 (2020)
2010 – 2019
- 2019
- [c57]Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How:
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. AAAI 2019: 7850-7857 - [c56]Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low, Mun Choon Chan:
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. CNS 2019: 91-99 - [c55]Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
Bayesian Optimization Meets Bayesian Optimal Stopping. ICML 2019: 1496-1506 - [c54]Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford:
Collective Model Fusion for Multiple Black-Box Experts. ICML 2019: 2742-2750 - [c53]Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan S. Kankanhalli:
Towards Robust ResNet: A Small Step but a Giant Leap. IJCAI 2019: 4285-4291 - [c52]Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN 2019: 1-8 - [c51]Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai:
Implicit Posterior Variational Inference for Deep Gaussian Processes. NeurIPS 2019: 14475-14486 - [c50]Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low:
Bayesian Optimization with Binary Auxiliary Information. UAI 2019: 1222-1232 - [i29]Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low, Mohan S. Kankanhalli:
Towards Robust ResNet: A Small Step but A Giant Leap. CoRR abs/1902.10887 (2019) - [i28]Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low, Mun Choon Chan:
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection. CoRR abs/1903.06661 (2019) - [i27]Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low:
Bayesian Optimization with Binary Auxiliary Information. CoRR abs/1906.07277 (2019) - [i26]Mohit Rajpal, Bryan Kian Hsiang Low:
A Unifying Framework of Bilinear LSTMs. CoRR abs/1910.10294 (2019) - [i25]Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet:
Implicit Posterior Variational Inference for Deep Gaussian Processes. CoRR abs/1910.11998 (2019) - [i24]Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet:
Inverse Reinforcement Learning with Missing Data. CoRR abs/1911.06930 (2019) - [i23]Tong Teng, Jie Chen, Yehong Zhang, Kian Hsiang Low:
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. CoRR abs/1912.02641 (2019) - 2018
- [c49]Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low:
Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. AAAI 2018: 3231-3238 - [c48]Ruofei Ouyang, Kian Hsiang Low:
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception. AAAI 2018: 3876-3883 - [i22]Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How:
Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems. CoRR abs/1805.09266 (2018) - 2017
- [c47]Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low:
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. AAAI 2017: 2007-2014 - [c46]Erik A. Daxberger, Bryan Kian Hsiang Low:
Distributed Batch Gaussian Process Optimization. ICML 2017: 951-960 - [i21]Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet:
Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. CoRR abs/1711.00221 (2017) - [i20]Ruofei Ouyang, Kian Hsiang Low:
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception. CoRR abs/1711.06064 (2017) - [i19]Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low:
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs. CoRR abs/1711.07033 (2017) - 2016
- [c45]Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet:
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. AAAI 2016: 1860-1866 - [c44]Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Near-Optimal Active Learning of Multi-Output Gaussian Processes. AAAI 2016: 2351-2357 - [c43]Chao Wang, Somchaya Liemhetcharat, Kian Hsiang Low:
Multi-Agent Continuous Transportation with Online Balanced Partitioning: (Extended Abstract). AAMAS 2016: 1303-1304 - [c42]Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. ICML 2016: 382-391 - [c41]Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, Tat-Seng Chua:
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks. IJCAI 2016: 1469-1475 - [c40]Yuhui Wang, Christian von der Weth, Yehong Zhang, Kian Hsiang Low, Vivek K. Singh, Mohan S. Kankanhalli:
Concept Based Hybrid Fusion of Multimodal Event Signals. ISM 2016: 14-19 - [i18]Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, Tat-Seng Chua:
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks. CoRR abs/1601.00917 (2016) - [i17]Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low:
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. CoRR abs/1611.06080 (2016) - 2015
- [j5]Jie Chen, Kian Hsiang Low, Yujian Yao, Patrick Jaillet:
Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems. IEEE Trans Autom. Sci. Eng. 12(3): 901-921 (2015) - [c39]Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet:
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation. AAAI 2015: 2821-2827 - [c38]Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. ICML 2015: 569-578 - [c37]Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet:
Inverse Reinforcement Learning with Locally Consistent Reward Functions. NIPS 2015: 1747-1755 - [i16]Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet:
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. CoRR abs/1511.06890 (2015) - [i15]Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Near-Optimal Active Learning of Multi-Output Gaussian Processes. CoRR abs/1511.06891 (2015) - [i14]Chao Wang, Somchaya Liemhetcharat, Kian Hsiang Low:
Multi-Agent Continuous Transportation with Online Balanced Partitioning. CoRR abs/1511.07209 (2015) - 2014
- [c36]Nuo Xu, Kian Hsiang Low, Jie Chen, Keng Kiat Lim, Etkin Baris Ozgul:
GP-Localize: Persistent Mobile Robot Localization Using Online Sparse Gaussian Process Observation Model. AAAI 2014: 2585-2593 - [c35]Etkin Baris Ozgul, Somchaya Liemhetcharat, Kian Hsiang Low:
Multi-agent ad hoc team partitioning by observing and modeling single-agent performance. APSIPA 2014: 1-7 - [c34]Ruofei Ouyang, Kian Hsiang Low, Jie Chen, Patrick Jaillet:
Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena. AAMAS 2014: 573-580 - [c33]Prabhu Natarajan, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-theoretic approach to maximizing fairness in multi-target observation in multi-camera surveillance. AAMAS 2014: 1521-1522 - [c32]Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu, Patrick Jaillet:
Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data. DyDESS 2014: 167-181 - [c31]Prabhu Natarajan, Kian Hsiang Low, Mohan S. Kankanhalli:
No One is Left "Unwatched": Fairness in Observation of Crowds of Mobile Targets in Active Camera Surveillance. ECAI 2014: 1155-1160 - [c30]Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low, Mohan S. Kankanhalli:
Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance. ICDSC 2014: 38:1-38:6 - [c29]Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli:
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. ICML 2014: 739-747 - [c28]Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli:
Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes. ECML/PKDD (3) 2014: 494-498 - [c27]Kian Hsiang Low, Nuo Xu, Jie Chen, Keng Kiat Lim, Etkin Baris Özgül:
Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization. ECML/PKDD (3) 2014: 499-503 - [i13]Nuo Xu, Kian Hsiang Low, Jie Chen, Keng Kiat Lim, Etkin Baris Ozgul:
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model. CoRR abs/1404.5165 (2014) - [i12]Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John M. Dolan, Gaurav S. Sukhatme:
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena. CoRR abs/1408.2046 (2014) - [i11]Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, Patrick Jaillet:
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. CoRR abs/1408.2060 (2014) - [i10]Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet:
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation. CoRR abs/1411.4510 (2014) - 2013
- [c26]Nannan Cao, Kian Hsiang Low, John M. Dolan:
Multi-robot informative path planning for active sensing of environmental phenomena: a tale of two algorithms. AAMAS 2013: 7-14 - [c25]