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ORCIDKian Hsiang Low
Bryan Kian Hsiang Low
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2020 – today
- 2024
[c128]Zhuanghua Liu, Bryan Kian Hsiang Low:
Decentralized Sum-of-Nonconvex Optimization. AAAI 2024: 14088-14096- [c127]Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low:
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI 2024: 14097-14105 - [c126]Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low:
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. AAAI 2024: 15373-15381 - [c125]Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low:
Position Paper: Data-Centric AI in the Age of Large Language Models. EMNLP (Findings) 2024: 11895-11913 - [c124]Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. EMNLP 2024: 20432-20466 - [c123]Rui Qiao, Bryan Kian Hsiang Low:
Understanding Domain Generalization: A Noise Robustness Perspective. ICLR 2024 - [c122]Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low:
Robustifying and Boosting Training-Free Neural Architecture Search. ICLR 2024 - [c121]Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low:
PINNACLE: PINN Adaptive ColLocation and Experimental points selection. ICLR 2024 - [c120]Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet:
Optimistic Bayesian Optimization with Unknown Constraints. ICLR 2024 - [c119]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. ICLR 2024 - [c118]Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
A Unified Framework for Bayesian Optimization under Contextual Uncertainty. ICLR 2024 - [c117]Zhaoxuan Wu
, Mohammad Mohammadi Amiri, Ramesh Raskar, Bryan Kian Hsiang Low:
Incentive-Aware Federated Learning with Training-Time Model Rewards. ICLR 2024 - [c116]Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. ICML 2024 - [c115]Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers. ICML 2024 - [c114]Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low:
Distributionally Robust Data Valuation. ICML 2024 - [c113]Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low:
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. ICML 2024 - [c112]Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low:
Deletion-Anticipative Data Selection with a Limited Budget. ICML 2024 - [c111]Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. ICML 2024 - [i92]Rui Qiao, Bryan Kian Hsiang Low:
Understanding Domain Generalization: A Noise Robustness Perspective. CoRR abs/2401.14846 (2024) - [i91]Zhuanghua Liu, Bryan Kian Hsiang Low:
Decentralized Sum-of-Nonconvex Optimization. CoRR abs/2402.02356 (2024) - [i90]Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low:
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR abs/2402.02359 (2024) - [i89]Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low:
Localized Zeroth-Order Prompt Optimization. CoRR abs/2403.02993 (2024) - [i88]Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low:
Robustifying and Boosting Training-Free Neural Architecture Search. CoRR abs/2403.07591 (2024) - [i87]Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. CoRR abs/2404.01676 (2024) - [i86]Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low:
PINNACLE: PINN Adaptive ColLocation and Experimental points selection. CoRR abs/2404.07662 (2024) - [i85]Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low:
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. CoRR abs/2405.14899 (2024) - [i84]Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. CoRR abs/2405.16122 (2024) - [i83]Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Prompt Optimization with Human Feedback. CoRR abs/2405.17346 (2024) - [i82]Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. CoRR abs/2406.04606 (2024) - [i81]Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low:
Data-Centric AI in the Age of Large Language Models. CoRR abs/2406.14473 (2024) - [i80]Nhung Bui, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low:
On Newton's Method to Unlearn Neural Networks. CoRR abs/2406.14507 (2024) - [i79]Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Waterfall: Framework for Robust and Scalable Text Watermarking. CoRR abs/2407.04411 (2024) - [i78]Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low:
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs. CoRR abs/2407.04981 (2024) - [i77]Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low:
Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models. CoRR abs/2407.14845 (2024) - [i76]Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low:
Neural Dueling Bandits. CoRR abs/2407.17112 (2024) - [i75]Lucas Agussurja, Xinyang Lu, Bryan Kian Hsiang Low:
Global-to-Local Support Spectrums for Language Model Explainability. CoRR abs/2408.05976 (2024) - [i74]Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low:
Online Fair Division with Contextual Bandits. CoRR abs/2408.12845 (2024) - [i73]Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Richard Yu:
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. CoRR abs/2409.06277 (2024) - [i72]Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti:
Data Distribution Valuation. CoRR abs/2410.04386 (2024) - [i71]Nithia Vijayan, Bryan Kian Hsiang Low:
Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework. CoRR abs/2412.06597 (2024) - [i70]Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low:
Paid with Models: Optimal Contract Design for Collaborative Machine Learning. CoRR abs/2412.11122 (2024) - [i69]Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low:
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks. CoRR abs/2412.15238 (2024) - [i68]Zijian Zhou, Xinyi Xu, Daniela Rus, Bryan Kian Hsiang Low:
Data value estimation on private gradients. CoRR abs/2412.17008 (2024) - 2023
- [j8]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) - [j7]Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low:
Pruning during training by network efficacy modeling. Mach. Learn. 112(7): 2653-2684 (2023) - [c110]Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Probably Approximate Shapley Fairness with Applications in Machine Learning. AAAI 2023: 5910-5918 - [c109]Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low:
No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. AISTATS 2023: 3591-3619 - [c108]Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low:
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. AISTATS 2023: 4033-4057 - [c107]Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low:
FedHQL: Federated Heterogeneous Q-Learning. AAMAS 2023: 2810-2812 - [c106]Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandits. ICLR 2023 - [c105]Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet:
Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. ICLR 2023 - [c104]Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low:
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. ICLR 2023 - [c103]Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low:
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. ICML 2023: 12931-12971 - [c102]Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Fair yet Asymptotically Equal Collaborative Learning. ICML 2023: 21223-21259 - [c101]Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low:
Collaborative Causal Inference with Fair Incentives. ICML 2023: 28300-28320 - [c100]Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Quantum Bayesian Optimization. NeurIPS 2023 - [c99]Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet:
Batch Bayesian Optimization For Replicable Experimental Design. NeurIPS 2023 - [c98]Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. NeurIPS 2023 - [c97]Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
Bayesian Optimization with Cost-varying Variable Subsets. NeurIPS 2023 - [c96]Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low:
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. NeurIPS 2023 - [c95]Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Model Shapley: Equitable Model Valuation with Black-box Access. NeurIPS 2023 - [i67]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) - [i66]Tiedong Liu, Bryan Kian Hsiang Low:
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks. CoRR abs/2305.14201 (2023) - [i65]Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low:
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. CoRR abs/2306.04454 (2023) - [i64]Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Fair yet Asymptotically Equal Collaborative Learning. CoRR abs/2306.05764 (2023) - [i63]Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low:
Hessian-Aware Bayesian Optimization for Decision Making Systems. CoRR abs/2308.00629 (2023) - [i62]Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low:
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. CoRR abs/2308.04077 (2023) - [i61]Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low:
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. CoRR abs/2310.00646 (2023) - [i60]Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers. CoRR abs/2310.02905 (2023) - [i59]Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Quantum Bayesian Optimization. CoRR abs/2310.05373 (2023) - [i58]Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet:
Batch Bayesian Optimization for Replicable Experimental Design. CoRR abs/2311.01195 (2023) - [i57]Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low:
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. CoRR abs/2311.02715 (2023) - [i56]Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low:
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. CoRR abs/2312.11413 (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, Daisuke Urano, 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 - [i55]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) - [i54]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) - [i53]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Rectified Max-Value Entropy Search for Bayesian Optimization. CoRR abs/2202.13597 (2022) - [i52]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) - [i51]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) - [i50]Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandit. CoRR abs/2205.14309 (2022) - [i49]Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On Provably Robust Meta-Bayesian Optimization. CoRR abs/2206.06872 (2022) - [i48]Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low:
Bayesian Optimization under Stochastic Delayed Feedback. CoRR abs/2206.09341 (2022) - [i47]Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR abs/2210.06850 (2022) - [i46]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 - [i45]Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet:
Convolutional Normalizing Flows for Deep Gaussian Processes. CoRR abs/2104.08472 (2021) - [i44]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Value-at-Risk Optimization with Gaussian Processes. CoRR abs/2105.06126 (2021) - [i43]Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. CoRR abs/2107.14465 (2021) - [i42]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) - [i41]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) - [i40]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) - [i39]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR abs/2110.14153 (2021) - [i38]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 - [i37]Dmitrii Kharkovskii, Chun Kai Ling, Kian Hsiang Low:
Nonmyopic Gaussian Process Optimization with Macro-Actions. CoRR abs/2002.09670 (2020) - [i36]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) - [i35]Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. CoRR abs/2010.10154 (2020) - [i34]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) - [i33]Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low:
Private Outsourced Bayesian Optimization. CoRR abs/2010.12799 (2020) - [i32]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. CoRR abs/2010.12883 (2020) - [i31]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) - [i30]Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet:
Top-k Ranking Bayesian Optimization. CoRR abs/2012.10688 (2020) - [i29]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 - [i28]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) - [i27]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) - [i26]Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low:
Bayesian Optimization with Binary Auxiliary Information. CoRR abs/1906.07277 (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]David R. Thompson, Nathalie Cabrol, P. Michael Furlong, Craig Hardgrove, Kian Hsiang Low, Jeffrey Moersch, David Wettergreen:
Adaptive sensing of time series with application to remote exploration. ICRA 2013: 3463-3468 - [c24]Trong Nghia Hoang, Kian Hsiang Low:
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. IJCAI 2013: 1394-1400 - [c23]Trong Nghia Hoang, Kian Hsiang Low:
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. IJCAI 2013: 2298-2305 - [c22]Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan:
Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System. Robotics: Science and Systems 2013 - [c21]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. UAI 2013 - [i9]Nannan Cao, Kian Hsiang Low, John M. Dolan:
Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms. CoRR abs/1302.0723 (2013) - [i8]Trong Nghia Hoang, Kian Hsiang Low:
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. CoRR abs/1304.2024 (2013) - [i7]Trong Nghia Hoang, Kian Hsiang Low:
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. CoRR abs/1304.5159 (2013) - [i6]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/1305.5826 (2013) - [i5]Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla:
Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing. CoRR abs/1305.6129 (2013) - [i4]Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan:
Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System. CoRR abs/1306.1491 (2013) - 2012
- [c20]Kian Hsiang Low, Jie Chen, John M. Dolan, Steve A. Chien, David R. Thompson:
Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing. AAMAS 2012: 105-112 - [c19]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance. AAMAS 2012: 155-162 - [c18]Trong Nghia Hoang, Kian Hsiang Low:
Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. AAMAS 2012: 1233-1234 - [c17]Jiangbo Yu, Kian Hsiang Low, Ali Oran, Patrick Jaillet:
Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents. IAT 2012: 478-485 - [c16]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments. ICDSC 2012: 1-6 - [c15]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. UAI 2012: 163-173 - [i3]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/1206.6230 (2012) - [i2]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments. CoRR abs/1209.4275 (2012) - 2011
- [c14]Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla:
Active Markov information-theoretic path planning for robotic environmental sensing. AAMAS 2011: 753-760 - [i1]Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla:
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing. CoRR abs/1101.5632 (2011)
2000 – 2009
- 2009
- [c13]Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla:
Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing. ICAPS 2009 - 2008
- [c12]Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla:
Adaptive multi-robot wide-area exploration and mapping. AAMAS (1) 2008: 23-30 - 2007
- [j4]Heng Wang, Kian Hsiang Low, Michael Yu Wang:
Virtual circle mapping for master-slave hand systems. Adv. Robotics 21(1): 183-208 (2007) - [c11]Kian Hsiang Low, Geoffrey J. Gordon, John M. Dolan, Pradeep K. Khosla:
Adaptive Sampling for Multi-Robot Wide-Area Exploration. ICRA 2007: 755-760 - [c10]Heng Wang, Kian Hsiang Low, Michael Yu Wang:
A Transparent Bilateral Controller for Teleoperation Considering the Transition of Motion. ICRA 2007: 4319-4324 - 2006
- [j3]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang:
Autonomic mobile sensor network with self-coordinated task allocation and execution. IEEE Trans. Syst. Man Cybern. Syst. 36(3): 315-327 (2006) - 2005
- [j2]Kian Hsiang Low, Heng Wang, Kim-Meow Liew, Yiyu Cai:
Modeling and Motion Control of Robotic Hand for Telemanipulation Application. Int. J. Softw. Eng. Knowl. Eng. 15(2): 147-152 (2005) - [j1]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.:
An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks. Neural Comput. 17(6): 1411-1445 (2005) - [c9]Heng Wang, Kian Hsiang Low, Michael Yu Wang, Feng Gong:
A Mapping Method for Telemanipulation of the Non-Anthropomorphic Robotic Hands with Initial Experimental Validation. ICRA 2005: 4218-4223 - 2004
- [c8]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.:
Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network. AAAI 2004: 28-33 - [c7]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.:
Reactive, Distributed Layered Architecture for Resource-bounded Multi-robot Cooperation: Application to Mobile Sensor Network Coverage. ICRA 2004: 3747-3752 - 2003
- [c6]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang:
Action selection in continuous state and action spaces by cooperation and competition of extended kohonen maps. AAMAS 2003: 1056-1057 - [c5]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.:
Enhancing the reactive capabilities of integrated planning and control with cooperative extended kohonen maps. ICRA 2003: 3428-3433 - [c4]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.:
Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps. IJCAI 2003: 1505-1506 - 2002
- [c3]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang:
A hybrid mobile robot architecture with integrated planning and control. AAMAS 2002: 219-226 - [c2]Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang:
Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network. ICRA 2002: 3870-3875 - 2001
- [c1]Debao Zhou, Kian Hsiang Low:
Combined Use of Ground Learning Model and Active Compliance to the Motion Control of Walking Robotic Legs. ICRA 2001: 3159-3164
Coauthor Index
[c125] [c122] [c115] [i89] [i88] [i84] [i83] [i81] [i76] [j8] [c107] [c106] [c104] [c103] [c100] [c99] [c96] [i67] [i65] [i62] [i61] [i60] [i59] [i58] [i57] [c91] [c88] [c85] [c83] [c82] [c81] [i55] [i52] [i50] [i49] [i48] [i47] [c77] [c74] [c73] [c72] [i44] [i42] [i41] [i40] [i39] [c64] [c62] [c59] [i36] [i35] [i33] [c55] [c51] [c50] [i26] [i25]
Chuan-Sheng Foo
aka: Chuan Sheng Foo
aka: Chuan Sheng Foo
[c120] [c119] [c115] [c112] [i87] [i84] [i83] [i76] [c106] [c105] [c104] [c100] [c99] [c98] [i60] [i59] [i58] [c85] [c84] [c82] [i53] [i50] [i49] [i47] [c80] [c79] [c78] [c77] [c76] [c75] [c73] [c72] [c69] [c68] [i45] [i44] [i43] [i39] [c64] [c63] [c59] [c58] [i36] [i35] [i32] [i30] [i29] [c55] [c52] [c51] [i25] [i24] [i21] [c45] [j5] [c39] [c37] [i16] [c34] [c32] [c29] [c28] [i12] [i11] [i10] [c21] [i6] [c17] [c15] [i3]
Sebastian Tay
aka: Sebastian Shenghong Tay
aka: Sebastian Shenghong Tay
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