Kian Hsiang Low

According to our database, Kian Hsiang Low authored at least 168 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Data value estimation on private gradients.

Zijian Zhou, Xinyi Xu, Daniela Rus, Bryan Kian Hsiang Low

CoRR, 2024

Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks.

Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low

CoRR, 2024

Paid with Models: Optimal Contract Design for Collaborative Machine Learning.

Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low

CoRR, 2024

Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework.

Nithia Vijayan, Bryan Kian Hsiang Low

CoRR, 2024

Data Distribution Valuation.

Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti

CoRR, 2024

Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models.

Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Richard Yu

CoRR, 2024

Online Fair Division with Contextual Bandits.

Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low

CoRR, 2024

Global-to-Local Support Spectrums for Language Model Explainability.

Lucas Agussurja, Xinyang Lu, Bryan Kian Hsiang Low

CoRR, 2024

Neural Dueling Bandits.

Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low

CoRR, 2024

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models.

Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low

CoRR, 2024

TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs.

Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low

CoRR, 2024

Waterfall: Framework for Robust and Scalable Text Watermarking.

Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

CoRR, 2024

On Newton's Method to Unlearn Neural Networks.

Nhung Bui, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low

CoRR, 2024

Data-Centric AI in the Age of Large Language Models.

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

CoRR, 2024

Prompt Optimization with Human Feedback.

Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low

CoRR, 2024

Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars.

Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low

CoRR, 2024

DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning.

Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low

CoRR, 2024

Incentives in Private Collaborative Machine Learning.

Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet

CoRR, 2024

Localized Zeroth-Order Prompt Optimization.

Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low

CoRR, 2024

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions.

Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Deletion-Anticipative Data Selection with a Limited Budget.

Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization.

Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Distributionally Robust Data Valuation.

Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Use Your INSTINCT: INSTruction optimization for LLMs 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components.

Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Incentive-Aware Federated Learning with Training-Time Model Rewards.

Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Unified Framework for Bayesian Optimization under Contextual Uncertainty.

Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Optimistic Bayesian Optimization with Unknown Constraints.

Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Twelfth International Conference on Learning Representations, 2024

PINNACLE: PINN Adaptive ColLocation and Experimental points selection.

Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Robustifying and Boosting Training-Free Neural Architecture Search.

Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Understanding Domain Generalization: A Noise Robustness Perspective.

Rui Qiao, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Position Paper: Data-Centric AI in the Age of Large Language Models.

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

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs.

Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning.

Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates.

Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Decentralized Sum-of-Nonconvex Optimization.

Zhuanghua Liu, Bryan Kian Hsiang Low

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Pruning during training by network efficacy modeling.

Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low

Mach. Learn., July, 2023

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

CoRR, 2023

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

CoRR, 2023

Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients.

Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low

CoRR, 2023

Hessian-Aware Bayesian Optimization for Decision Making Systems.

Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low

CoRR, 2023

Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks.

Tiedong Liu, Bryan Kian Hsiang Low

CoRR, 2023

FedHQL: Federated Heterogeneous Q-Learning.

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer

CoRR, 2023

Recursive reasoning-based training-time adversarial machine learning.

Yizhou Chen, Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Teck-Hua Ho

Artif. Intell., 2023

Model Shapley: Equitable Model Valuation with Black-box Access.

Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits.

Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Bayesian Optimization with Cost-varying Variable Subsets.

Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Incentives in Private Collaborative Machine Learning.

Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Batch Bayesian Optimization For Replicable Experimental Design.

Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Quantum Bayesian Optimization.

Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fair yet Asymptotically Equal Collaborative Learning.

Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2023

Training-Free Neural Active Learning with Initialization-Robustness Guarantees.

Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2023

Collaborative Causal Inference with Fair Incentives.

Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2023

Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation.

Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation.

Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Neural Bandits.

Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

FedHQL: Federated Heterogeneous Q-Learning.

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery.

Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities.

Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Probably Approximate Shapley Fairness with Applications in Machine Learning.

Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Federated Neural Bandit.

Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet

CoRR, 2022

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization.

Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng

CoRR, 2022

Rectified Max-Value Entropy Search for Bayesian Optimization.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

CoRR, 2022

Neural ensemble search via Bayesian sampling.

Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Uncertainty in Artificial Intelligence, 2022

On provably robust meta-Bayesian optimization.

Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.

Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sample-Then-Optimize Batch Neural Thompson Sampling.

Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges.

Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

DAVINZ: Data Valuation using Deep Neural Networks at Initialization.

Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2022

Bayesian Optimization under Stochastic Delayed Feedback.

Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2022

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity.

Sebastian Shenghong Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2022

On the Convergence of the Shapley Value in Parametric Bayesian Learning Games.

Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2022

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization.

Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low

Proceedings of the Tenth International Conference on Learning Representations, 2022

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten.

Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran, Mun Choon Chan, Bryan Kian Hsiang Low

Proceedings of the ASIA CCS '22: ACM Asia Conference on Computer and Communications Security, Nagasaki, Japan, 30 May 2022, 2022

Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning.

Yizhou Chen, Shizhuo Zhang, Bryan Kian Hsiang Low

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards.

Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search.

Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low

CoRR, 2021

Trusted-maximizers entropy search for efficient Bayesian optimization.

Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Learning to learn with Gaussian processes.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Validation Free and Replication Robust Volume-based Data Valuation.

Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning.

Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimizing Conditional Value-At-Risk of Black-Box Functions.

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee.

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially Private Federated Bayesian Optimization with Distributed Exploration.

Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Convolutional Normalizing Flows for Deep Gaussian Processes.

Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the International Joint Conference on Neural Networks, 2021

Collaborative Bayesian Optimization with Fair Regret.

Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 38th International Conference on Machine Learning, 2021

Value-at-Risk Optimization with Gaussian Processes.

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 38th International Conference on Machine Learning, 2021

Model Fusion for Personalized Learning.

Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 38th International Conference on Machine Learning, 2021

Top-k Ranking Bayesian Optimization.

Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

An Information-Theoretic Framework for Unifying Active Learning Problems.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception.

Ruofei Ouyang, Bryan Kian Hsiang Low

Auton. Robots, 2020

Variational Bayesian Unlearning.

Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Bayesian Optimization via Thompson Sampling.

Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization.

Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Collaborative Machine Learning with Incentive-Aware Model Rewards.

Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low

Proceedings of the 37th International Conference on Machine Learning, 2020

Private Outsourced Bayesian Optimization.

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion.

Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 37th International Conference on Machine Learning, 2020

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games.

Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho

Proceedings of the 37th International Conference on Machine Learning, 2020

FCM-sketch: generic network measurements with data plane support.

Cha Hwan Song, Pravein Govindan Kannan, Bryan Kian Hsiang Low, Mun Choon Chan

Proceedings of the CoNEXT '20: The 16th International Conference on emerging Networking EXperiments and Technologies, 2020

Nonmyopic Gaussian Process Optimization with Macro-Actions.

Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression.

Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Inverse Reinforcement Learning with Missing Data.

Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet

CoRR, 2019

Bayesian Optimization with Binary Auxiliary Information.

Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Implicit Posterior Variational Inference for Deep Gaussian Processes.

Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression.

Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the International Joint Conference on Neural Networks, 2019

Towards Robust ResNet: A Small Step but a Giant Leap.

Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Collective Model Fusion for Multiple Black-Box Experts.

Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford

Proceedings of the 36th International Conference on Machine Learning, 2019

Bayesian Optimization Meets Bayesian Optimal Stopping.

Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Proceedings of the 7th IEEE Conference on Communications and Network Security, 2019

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems.

Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems.

Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How

CoRR, 2018

Decentralized High-Dimensional Bayesian Optimization With Factor Graphs.

Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models.

Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet

CoRR, 2017

Distributed Batch Gaussian Process Optimization.

Erik A. Daxberger, Bryan Kian Hsiang Low

Proceedings of the 34th International Conference on Machine Learning, 2017

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression.

Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Concept Based Hybrid Fusion of Multimodal Event Signals.

Yuhui Wang, Christian von der Weth, Yehong Zhang, Kian Hsiang Low, Vivek K. Singh, Mohan S. Kankanhalli

Proceedings of the IEEE International Symposium on Multimedia, 2016

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

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models.

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Proceedings of the 33nd International Conference on Machine Learning, 2016

Multi-Agent Continuous Transportation with Online Balanced Partitioning: (Extended Abstract).

Chao Wang, Somchaya Liemhetcharat, Kian Hsiang Low

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

Near-Optimal Active Learning of Multi-Output Gaussian Processes.

Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond.

Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems.

Jie Chen, Kian Hsiang Low, Yujian Yao, Patrick Jaillet

IEEE Trans Autom. Sci. Eng., 2015

Multi-Agent Continuous Transportation with Online Balanced Partitioning.

Chao Wang, Somchaya Liemhetcharat, Kian Hsiang Low

CoRR, 2015

Inverse Reinforcement Learning with Locally Consistent Reward Functions.

Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data.

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Proceedings of the 32nd International Conference on Machine Learning, 2015

Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation.

Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization.

Kian Hsiang Low, Nuo Xu, Jie Chen, Keng Kiat Lim, Etkin Baris Özgül

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes.

Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes.

Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli

Proceedings of the 31th International Conference on Machine Learning, 2014

Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance.

Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the International Conference on Distributed Smart Cameras, 2014

No One is Left "Unwatched": Fairness in Observation of Crowds of Mobile Targets in Active Camera Surveillance.

Prabhu Natarajan, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic, 2014

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

Proceedings of the Dynamic Data-Driven Environmental Systems Science, 2014

Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena.

Ruofei Ouyang, Kian Hsiang Low, Jie Chen, Patrick Jaillet

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Decision-theoretic approach to maximizing fairness in multi-target observation in multi-camera surveillance.

Prabhu Natarajan, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Multi-agent ad hoc team partitioning by observing and modeling single-agent performance.

Etkin Baris Ozgul, Somchaya Liemhetcharat, Kian Hsiang Low

Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2014

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

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

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

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System.

Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan

Proceedings of the Robotics: Science and Systems IX, Technische Universität Berlin, Berlin, Germany, June 24, 2013

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents.

Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the IJCAI 2013, 2013

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior.

Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the IJCAI 2013, 2013

Adaptive sensing of time series with application to remote exploration.

David R. Thompson, Nathalie Cabrol, P. Michael Furlong, Craig Hardgrove, Kian Hsiang Low, Jeffrey Moersch, David Wettergreen

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Multi-robot informative path planning for active sensing of environmental phenomena: a tale of two algorithms.

Nannan Cao, Kian Hsiang Low, John M. Dolan

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2013

2012

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

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments.

Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the Sixth International Conference on Distributed Smart Cameras, 2012

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

Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Intelligent Agent Technology, 2012

Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance.

Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing.

Kian Hsiang Low, Jie Chen, John M. Dolan, Steve A. Chien, David R. Thompson

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents.

Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

2011

Active Markov information-theoretic path planning for robotic environmental sensing.

Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

2009

Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing.

Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla

Proceedings of the 19th International Conference on Automated Planning and Scheduling, 2009

2008

Adaptive multi-robot wide-area exploration and mapping.

Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla

Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008

2007

Virtual circle mapping for master-slave hand systems.

Heng Wang, Kian Hsiang Low, Michael Yu Wang

Adv. Robotics, 2007

A Transparent Bilateral Controller for Teleoperation Considering the Transition of Motion.

Heng Wang, Kian Hsiang Low, Michael Yu Wang

Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

Adaptive Sampling for Multi-Robot Wide-Area Exploration.

Kian Hsiang Low, Geoffrey J. Gordon, John M. Dolan, Pradeep K. Khosla

Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

2006

Autonomic mobile sensor network with self-coordinated task allocation and execution.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang

IEEE Trans. Syst. Man Cybern. Syst., 2006

2005

An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.

Neural Comput., 2005

Modeling and Motion Control of Robotic Hand for Telemanipulation Application.

Kian Hsiang Low, Heng Wang, Kim-Meow Liew, Yiyu Cai

Int. J. Softw. Eng. Knowl. Eng., 2005

A Mapping Method for Telemanipulation of the Non-Anthropomorphic Robotic Hands with Initial Experimental Validation.

Heng Wang, Kian Hsiang Low, Michael Yu Wang, Feng Gong

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

2004

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.

Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004

Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.

Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004

2003

Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.

Proceedings of the IJCAI-03, 2003

Enhancing the reactive capabilities of integrated planning and control with cooperative extended kohonen maps.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr.

Proceedings of the 2003 IEEE International Conference on Robotics and Automation, 2003

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

Proceedings of the Second International Joint Conference on Autonomous Agents & Multiagent Systems, 2003

2002

Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang

Proceedings of the 2002 IEEE International Conference on Robotics and Automation, 2002

A hybrid mobile robot architecture with integrated planning and control.

Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang

Proceedings of the First International Joint Conference on Autonomous Agents & Multiagent Systems, 2002

2001

Combined Use of Ground Learning Model and Active Compliance to the Motion Control of Walking Robotic Legs.

Debao Zhou, Kian Hsiang Low

Proceedings of the 2001 IEEE International Conference on Robotics and Automation, 2001