Combined Use of Ground Learning Model and Active Compliance to the Motion Control of Walking Robotic Legs. - Debao Zhou; Kian Hsiang Low (ICRA, 2001)

A hybrid mobile robot architecture with integrated planning and control. - Kian Hsiang Low; Wee Kheng Leow; Marcelo H. Ang (AAMAS, 2002)

Integrated Planning and Control of Mobile Robot with Self-Organizing Neural Network. - Kian Hsiang Low; Wee Kheng Leow; Marcelo H. Ang (ICRA, 2002)

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 (AAMAS, 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. (ICRA, 2003)

Action Selection for Single- and Multi-Robot Tasks Using Cooperative Extended Kohonen Maps. - Kian Hsiang Low; Wee Kheng Leow; Marcelo H. Ang Jr. (IJCAI, 2003)

Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network. - Kian Hsiang Low; Wee Kheng Leow; Marcelo H. Ang Jr. (AAAI, 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. (ICRA, 2004)

A Mapping Method for Telemanipulation of the Non-Anthropomorphic Robotic Hands with Initial Experimental Validation. - Heng Wang 001; Kian Hsiang Low; Michael Yu Wang; Feng Gong (ICRA, 2005)

Modeling and Motion Control of Robotic Hand for Telemanipulation Application. - Kian Hsiang Low; Heng Wang 001; Kim-Meow Liew; Yiyu Cai (Int. J. Softw. Eng. Knowl. Eng., 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)

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)

Adaptive Sampling for Multi-Robot Wide-Area Exploration. - Kian Hsiang Low; Geoffrey J. Gordon; John M. Dolan; Pradeep K. Khosla (ICRA, 2007)

A Transparent Bilateral Controller for Teleoperation Considering the Transition of Motion. - Heng Wang 001; Kian Hsiang Low; Michael Yu Wang (ICRA, 2007)

Virtual circle mapping for master-slave hand systems. - Heng Wang 001; Kian Hsiang Low; Michael Yu Wang (Adv. Robotics, 2007)

Adaptive multi-robot wide-area exploration and mapping. - Kian Hsiang Low; John M. Dolan; Pradeep K. Khosla (AAMAS (1), 2008)

Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing. - Kian Hsiang Low; John M. Dolan; Pradeep K. Khosla (ICAPS, 2009)

Active Markov information-theoretic path planning for robotic environmental sensing. - Kian Hsiang Low; John M. Dolan; Pradeep K. Khosla (AAMAS, 2011)

Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. - Trong Nghia Hoang; Kian Hsiang Low (AAMAS, 2012)

Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing. - Kian Hsiang Low; Jie Chen 027; John M. Dolan; Steve A. Chien; David R. Thompson 001 (AAMAS, 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 (AAMAS, 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 (IAT, 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 (ICDSC, 2012)

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena. - Jie Chen 027; Kian Hsiang Low; Colin Keng-Yan Tan; Ali Oran; Patrick Jaillet; John M. Dolan; Gaurav S. Sukhatme (UAI, 2012)

Multi-robot informative path planning for active sensing of environmental phenomena: a tale of two algorithms. - Nannan Cao; Kian Hsiang Low; John M. Dolan (AAMAS, 2013)

Adaptive sensing of time series with application to remote exploration. - David R. Thompson 001; Nathalie Cabrol; P. Michael Furlong; Craig Hardgrove; Kian Hsiang Low; Jeffrey Moersch; David Wettergreen (ICRA, 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 (IJCAI, 2013)

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. - Trong Nghia Hoang; Kian Hsiang Low (IJCAI, 2013)

Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System. - Jie Chen 027; Kian Hsiang Low; Colin Keng-Yan Tan (Robotics: Science and Systems, 2013)

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. - Jie Chen 027; Nannan Cao; Kian Hsiang Low; Ruofei Ouyang; Colin Keng-Yan Tan; Patrick Jaillet (UAI, 2013)

GP-Localize: Persistent Mobile Robot Localization Using Online Sparse Gaussian Process Observation Model. - Nuo Xu; Kian Hsiang Low; Jie Chen 027; Keng Kiat Lim; Etkin Baris Ozgul (AAAI, 2014)

Multi-agent ad hoc team partitioning by observing and modeling single-agent performance. - Etkin Baris Ozgul; Somchaya Liemhetcharat; Kian Hsiang Low (APSIPA, 2014)

Decision-theoretic approach to maximizing fairness in multi-target observation in multi-camera surveillance. - Prabhu Natarajan; Kian Hsiang Low; Mohan S. Kankanhalli (AAMAS, 2014)

Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena. - Ruofei Ouyang; Kian Hsiang Low; Jie Chen 027; Patrick Jaillet (AAMAS, 2014)

Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data. - Kian Hsiang Low; Jie Chen 027; Trong Nghia Hoang; Nuo Xu; Patrick Jaillet (DyDESS, 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 (ECAI, 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 (ICDSC, 2014)

Nonmyopic ( epsilon )-Bayes-Optimal Active Learning of Gaussian Processes. - Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet; Mohan S. Kankanhalli (ICML, 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 (ECML/PKDD (3), 2014)

Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization. - Kian Hsiang Low; Nuo Xu; Jie Chen 027; Keng Kiat Lim; Etkin Baris Özgül (ECML/PKDD (3), 2014)

Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation. - Kian Hsiang Low; Jiangbo Yu; Jie Chen 027; Patrick Jaillet (AAAI, 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 (ICML, 2015)

Inverse Reinforcement Learning with Locally Consistent Reward Functions. - Quoc Phong Nguyen; Kian Hsiang Low; Patrick Jaillet (NIPS, 2015)

Multi-Agent Continuous Transportation with Online Balanced Partitioning. - Chao Wang; Somchaya Liemhetcharat; Kian Hsiang Low (CoRR, 2015)

Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems. - Jie Chen 027; Kian Hsiang Low; Yujian Yao; Patrick Jaillet (IEEE Trans Autom. Sci. Eng., 2015)

Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. - Chun Kai Ling; Kian Hsiang Low; Patrick Jaillet (AAAI, 2016)

Near-Optimal Active Learning of Multi-Output Gaussian Processes. - Yehong Zhang; Trong Nghia Hoang; Kian Hsiang Low; Mohan S. Kankanhalli (AAAI, 2016)

Multi-Agent Continuous Transportation with Online Balanced Partitioning: (Extended Abstract). - Chao Wang; Somchaya Liemhetcharat; Kian Hsiang Low (AAMAS, 2016)

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. - Trong Nghia Hoang; Quang Minh Hoang; Bryan Kian Hsiang Low (ICML, 2016)

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks. - Jie Fu 001; Hongyin Luo; Jiashi Feng; Kian Hsiang Low; Tat-Seng Chua (IJCAI, 2016)

Concept Based Hybrid Fusion of Multimodal Event Signals. - Yuhui Wang; Christian von der Weth; Yehong Zhang; Kian Hsiang Low; Vivek K. Singh 001; Mohan S. Kankanhalli (ISM, 2016)

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. - Quang Minh Hoang; Trong Nghia Hoang; Kian Hsiang Low (AAAI, 2017)

Distributed Batch Gaussian Process Optimization. - Erik A. Daxberger; Bryan Kian Hsiang Low (ICML, 2017)

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. - Haibin Yu; Trong Nghia Hoang; Kian Hsiang Low; Patrick Jaillet (CoRR, 2017)

Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. - Trong Nghia Hoang; Quang Minh Hoang; Ruofei Ouyang; Kian Hsiang Low (AAAI, 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)

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. - Trong Nghia Hoang; Quang Minh Hoang; Kian Hsiang Low; Jonathan P. How (AAAI, 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 (CNS, 2019)

Bayesian Optimization Meets Bayesian Optimal Stopping. - Zhongxiang Dai; Haibin Yu; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2019)

Collective Model Fusion for Multiple Black-Box Experts. - Quang Minh Hoang; Trong Nghia Hoang; Bryan Kian Hsiang Low; Carl Kingsford (ICML, 2019)

Towards Robust ResNet: A Small Step but a Giant Leap. - Jingfeng Zhang; Bo Han 003; Laura Wynter; Bryan Kian Hsiang Low; Mohan S. Kankanhalli (IJCAI, 2019)

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. - Haibin Yu; Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet (IJCNN, 2019)

Implicit Posterior Variational Inference for Deep Gaussian Processes. - Haibin Yu; Yizhou Chen; Bryan Kian Hsiang Low; Patrick Jaillet; Zhongxiang Dai (NeurIPS, 2019)

Bayesian Optimization with Binary Auxiliary Information. - Yehong Zhang; Zhongxiang Dai; Bryan Kian Hsiang Low (UAI, 2019)

Inverse Reinforcement Learning with Missing Data. - Tien Mai; Quoc Phong Nguyen; Kian Hsiang Low; Patrick Jaillet (CoRR, 2019)

Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. - Tong Teng; Jie Chen 027; Yehong Zhang; Bryan Kian Hsiang Low (AAAI, 2020)

Nonmyopic Gaussian Process Optimization with Macro-Actions. - Dmitrii Kharkovskii; Chun Kai Ling; Bryan Kian Hsiang Low (AISTATS, 2020)

FCM-sketch: generic network measurements with data plane support. - Cha Hwan Song; Pravein Govindan Kannan; Bryan Kian Hsiang Low; Mun Choon Chan (CoNEXT, 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 (ICML, 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 (ICML, 2020)

Private Outsourced Bayesian Optimization. - Dmitrii Kharkovskii; Zhongxiang Dai; Bryan Kian Hsiang Low (ICML, 2020)

Collaborative Machine Learning with Incentive-Aware Model Rewards. - Rachael Hwee Ling Sim; Yehong Zhang; Mun Choon Chan; Bryan Kian Hsiang Low (ICML, 2020)

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. - Sreejith Balakrishnan; Quoc Phong Nguyen; Bryan Kian Hsiang Low; Harold Soh (NeurIPS, 2020)

Federated Bayesian Optimization via Thompson Sampling. - Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2020)

Variational Bayesian Unlearning. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2020)

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception. - Ruofei Ouyang; Bryan Kian Hsiang Low (Auton. Robots, 2020)

An Information-Theoretic Framework for Unifying Active Learning Problems. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (AAAI, 2021)

Top-k Ranking Bayesian Optimization. - Quoc Phong Nguyen; Sebastian Tay; Bryan Kian Hsiang Low; Patrick Jaillet (AAAI, 2021)

Model Fusion for Personalized Learning. - Thanh Chi Lam; Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2021)

Value-at-Risk Optimization with Gaussian Processes. - Quoc Phong Nguyen; Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2021)

Collaborative Bayesian Optimization with Fair Regret. - Rachael Hwee Ling Sim; Yehong Zhang; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2021)

Convolutional Normalizing Flows for Deep Gaussian Processes. - Haibin Yu; Dapeng Liu; Bryan Kian Hsiang Low; Patrick Jaillet (IJCNN, 2021)

Differentially Private Federated Bayesian Optimization with Distributed Exploration. - Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2021)

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Wei Jing; Cheston Tan; Bryan Kian Hsiang Low (NeurIPS, 2021)

Optimizing Conditional Value-At-Risk of Black-Box Functions. - Quoc Phong Nguyen; Zhongxiang Dai; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 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 (NeurIPS, 2021)

Validation Free and Replication Robust Volume-based Data Valuation. - Xinyi Xu; Zhaoxuan Wu; Chuan Sheng Foo; Bryan Kian Hsiang Low (NeurIPS, 2021)

Learning to learn with Gaussian processes. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (UAI, 2021)

Trusted-maximizers entropy search for efficient Bayesian optimization. - Quoc Phong Nguyen; Zhaoxuan Wu; Bryan Kian Hsiang Low; Patrick Jaillet (UAI, 2021)

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search. - Yao Shu; Yizhou Chen; Zhongxiang Dai; Bryan Kian Hsiang Low (CoRR, 2021)

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. - Sebastian Shenghong Tay; Xinyi Xu; Chuan Sheng Foo; Bryan Kian Hsiang Low (AAAI, 2022)

Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning. - Yizhou Chen; Shizhuo Zhang; Bryan Kian Hsiang Low (AISTATS, 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 (AsiaCCS, 2022)

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. - Yao Shu; Shaofeng Cai; Zhongxiang Dai; Beng Chin Ooi; Bryan Kian Hsiang Low (ICLR, 2022)

On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. - Lucas Agussurja; Xinyi Xu; Bryan Kian Hsiang Low (ICML, 2022)

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. - Sebastian Shenghong Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICML, 2022)

Bayesian Optimization under Stochastic Delayed Feedback. - Arun Verma; Zhongxiang Dai; Bryan Kian Hsiang Low (ICML, 2022)

DAVINZ: Data Valuation using Deep Neural Networks at Initialization. - Zhaoxuan Wu; Yao Shu; Bryan Kian Hsiang Low (ICML, 2022)

Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges. - Rachael Hwee Ling Sim; Xinyi Xu; Bryan Kian Hsiang Low (IJCAI, 2022)

Sample-Then-Optimize Batch Neural Thompson Sampling. - Zhongxiang Dai; Yao Shu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2022)

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2022)

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. - Yao Shu; Zhongxiang Dai; Zhaoxuan Wu; Bryan Kian Hsiang Low (NeurIPS, 2022)

On provably robust meta-Bayesian optimization. - Zhongxiang Dai; Yizhou Chen; Haibin Yu; Bryan Kian Hsiang Low; Patrick Jaillet (UAI, 2022)

Neural ensemble search via Bayesian sampling. - Yao Shu; Yizhou Chen; Zhongxiang Dai; Bryan Kian Hsiang Low (UAI, 2022)

Rectified Max-Value Entropy Search for Bayesian Optimization. - Quoc Phong Nguyen; 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)

Federated Neural Bandit. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (CoRR, 2022)

Probably Approximate Shapley Fairness with Applications in Machine Learning. - Zijian Zhou; Xinyi Xu; Rachael Hwee Ling Sim; Chuan Sheng Foo; Bryan Kian Hsiang Low (AAAI, 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 (AISTATS, 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 (AISTATS, 2023)

FedHQL: Federated Heterogeneous Q-Learning. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Cheston Tan; Bryan Kian Hsiang Low (AAMAS, 2023)

Federated Neural Bandits. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2023)

Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. - Thanh Lam; Arun Verma; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2023)

Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. - Yao Shu; Zhongxiang Dai; Weicong Sng; Arun Verma; Patrick Jaillet; Bryan Kian Hsiang Low (ICLR, 2023)

Collaborative Causal Inference with Fair Incentives. - Rui Qiao 006; Xinyi Xu; Bryan Kian Hsiang Low (ICML, 2023)

Training-Free Neural Active Learning with Initialization-Robustness Guarantees. - Apivich Hemachandra; Zhongxiang Dai; Jasraj Singh; See-Kiong Ng; Bryan Kian Hsiang Low (ICML, 2023)

Fair yet Asymptotically Equal Collaborative Learning. - Xiaoqiang Lin; Xinyi Xu; See-Kiong Ng; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 2023)

Quantum Bayesian Optimization. - Zhongxiang Dai; Gregory Kang Ruey Lau; Arun Verma; Yao Shu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 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 (NeurIPS, 2023)

Incentives in Private Collaborative Machine Learning. - Rachael Hwee Ling Sim; Yehong Zhang; Nghia Hoang; Xinyi Xu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2023)

Bayesian Optimization with Cost-varying Variable Subsets. - Sebastian Tay; Chuan Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (NeurIPS, 2023)

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. - Arun Verma; Zhongxiang Dai; Yao Shu; Bryan Kian Hsiang Low (NeurIPS, 2023)

Model Shapley: Equitable Model Valuation with Black-box Access. - Xinyi Xu; Thanh Lam; Chuan Sheng Foo; Bryan Kian Hsiang Low (NeurIPS, 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)

FedHQL: Federated Heterogeneous Q-Learning. - Flint Xiaofeng Fan; Yining Ma 001; Zhongxiang Dai; Cheston Tan; Bryan Kian Hsiang Low; Roger Wattenhofer (CoRR, 2023)

Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks. - Tiedong Liu; 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)

Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. - Yao Shu; Xiaoqiang Lin; Zhongxiang Dai; Bryan Kian Hsiang Low (CoRR, 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)

Pruning during training by network efficacy modeling. - Mohit Rajpal; Yehong Zhang; Bryan Kian Hsiang Low (Mach. Learn., 2023)

Decentralized Sum-of-Nonconvex Optimization. - Zhuanghua Liu; Bryan Kian Hsiang Low (AAAI, 2024)

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. - Zhuanghua Liu; Luo Luo; Bryan Kian Hsiang Low (AAAI, 2024)

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. - Xiao Tian; Rachael Hwee Ling Sim; Jue Fan; Bryan Kian Hsiang Low (AAAI, 2024)

Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. - Gregory Kang Ruey Lau; Xinyuan Niu 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (EMNLP, 2024)

Position Paper: Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (EMNLP (Findings), 2024)

Understanding Domain Generalization: A Noise Robustness Perspective. - Rui Qiao 006; Bryan Kian Hsiang Low (ICLR, 2024)

Robustifying and Boosting Training-Free Neural Architecture Search. - Zhenfeng He; Yao Shu; Zhongxiang Dai; Bryan Kian Hsiang Low (ICLR, 2024)

PINNACLE: PINN Adaptive ColLocation and Experimental points selection. - Gregory Kang Ruey Lau; Apivich Hemachandra; See-Kiong Ng; Bryan Kian Hsiang Low (ICLR, 2024)

Optimistic Bayesian Optimization with Unknown Constraints. - Quoc Phong Nguyen; Wan Theng Ruth Chew; Le Song; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2024)

Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. - Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2024)

A Unified Framework for Bayesian Optimization under Contextual Uncertainty. - Sebastian Shenghong Tay; Chuan-Sheng Foo; Daisuke Urano; Richalynn Leong; Bryan Kian Hsiang Low (ICLR, 2024)

Incentive-Aware Federated Learning with Training-Time Model Rewards. - Zhaoxuan Wu; Mohammad Mohammadi Amiri; Ramesh Raskar; Bryan Kian Hsiang Low (ICLR, 2024)

Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. - Zhiliang Chen; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 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 (ICML, 2024)

Distributionally Robust Data Valuation. - Xiaoqiang Lin; Xinyi Xu; Zhaoxuan Wu; See-Kiong Ng; Bryan Kian Hsiang Low (ICML, 2024)

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. - Zhuanghua Liu; Cheng Chen 015; Luo Luo; Bryan Kian Hsiang Low (ICML, 2024)

Deletion-Anticipative Data Selection with a Limited Budget. - Rachael Hwee Ling Sim; Jue Fan; Xiao Tian; Patrick Jaillet; Bryan Kian Hsiang Low (ICML, 2024)

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. - Jingtan Wang 001; Xiaoqiang Lin; Rui Qiao 006; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICML, 2024)

Localized Zeroth-Order Prompt Optimization. - Wenyang Hu; Yao Shu; Zongmin Yu; Zhaoxuan Wu; Xiaoqiang Lin; Zhongxiang Dai; See-Kiong Ng; Bryan Kian Hsiang Low (NeurIPS, 2024)

Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization. - Zhuanghua Liu; Luo Luo; Bryan Kian Hsiang Low (NeurIPS, 2024)

Active Set Ordering. - Quoc Phong Nguyen; Sunil Gupta 001; Svetha Venkatesh; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 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 (NeurIPS, 2024)

Data Distribution Valuation. - Xinyi Xu; Shuaiqi Wang; Chuan Sheng Foo; Bryan Kian Hsiang Low; Giulia Fanti (NeurIPS, 2024)

DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. - Zijian Zhou; Xiaoqiang Lin; Xinyi Xu; Alok Prakash; Daniela Rus; Bryan Kian Hsiang Low (NeurIPS, 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)

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)

Prompt Optimization with Human Feedback. - Xiaoqiang Lin; Zhongxiang Dai; Arun Verma; See-Kiong Ng; Patrick Jaillet; Bryan Kian Hsiang Low (CoRR, 2024)

Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (CoRR, 2024)

On Newton's Method to Unlearn Neural Networks. - Nhung Bui; 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 001; Hieu Dao; Jiangwei Chen 001; Chuan-Sheng Foo; 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)

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)

Neural Dueling Bandits. - Arun Verma; Zhongxiang Dai; Xiaoqiang Lin; Patrick Jaillet; 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)

Online Fair Division with Contextual Bandits. - Arun Verma; Indrajit Saha; Makoto Yokoo; Bryan Kian Hsiang Low (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)

Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework. - Nithia Vijayan; 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)

Data value estimation on private gradients. - Zijian Zhou; Xinyi Xu; Daniela Rus; Bryan Kian Hsiang Low (CoRR, 2024)

Dependency Structure Search Bayesian Optimization for Decision Making Models. - Mohit Rajpal; Lac Gia Tran; Yehong Zhang; Bryan Kian Hsiang Low (Trans. Mach. Learn. Res., 2024)

Paid with Models: Optimal Contract Design for Collaborative Machine Learning. - Bingchen Wang; Zhaoxuan Wu; Fusheng Liu; Bryan Kian Hsiang Low (AAAI, 2025)

WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. - Xinyang Lu; Jingtan Wang 001; Zitong Zhao; Zhongxiang Dai; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low (ACL (Findings), 2025)

TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding. - Zhaoxuan Wu; Zijian Zhou; Arun Verma; Alok Prakash; Daniela Rus; Bryan Kian Hsiang Low (ACL (1), 2025)

Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions. - Rui Qiao 006; Zhaoxuan Wu; Jingtan Wang 001; Pang Wei Koh; Bryan Kian Hsiang Low (ICLR, 2025)

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space. - Zhiliang Chen; Xinyuan Niu 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (ICLR, 2025)

PIED: Physics-Informed Experimental Design for Inverse Problems. - Apivich Hemachandra; Gregory Kang Ruey Lau; See-Kiong Ng; Bryan Kian Hsiang Low (ICLR, 2025)

Efficient Top-m Data Values Identification for Data Selection. - Xiaoqiang Lin; Xinyi Xu; See-Kiong Ng; Bryan Kian Hsiang Low (ICLR, 2025)

Neural Dueling Bandits: Preference-Based Optimization with Human Feedback. - Arun Verma; Zhongxiang Dai; Xiaoqiang Lin; Patrick Jaillet; Bryan Kian Hsiang Low (ICLR, 2025)

DUPRE: Data Utility Prediction for Efficient Data Valuation. - Kieu Thao Nguyen Pham; Rachael Hwee Ling Sim; Quoc Phong Nguyen; See-Kiong Ng; Bryan Kian Hsiang Low (AAMAS, 2025)

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks. - Zhiliang Chen; Gregory Kang Ruey Lau; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2025)

BILBO: BILevel Bayesian Optimization. - Wan Theng Ruth Chew; Quoc Phong Nguyen; Bryan Kian Hsiang Low (CoRR, 2025)

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs using Semantic Space. - Zhiliang Chen; Xinyuan Niu 001; Chuan-Sheng Foo; Bryan Kian Hsiang Low (CoRR, 2025)

Active Human Feedback Collection via Neural Contextual Dueling Bandits. - Arun Verma; Xiaoqiang Lin; Zhongxiang Dai; Daniela Rus; Bryan Kian Hsiang Low (CoRR, 2025)

ReasonIR: Training Retrievers for Reasoning Tasks. - Rulin Shao; Rui Qiao 006; Varsha Kishore; Niklas Muennighoff; Xi Victoria Lin; Daniela Rus; Bryan Kian Hsiang Low; Sewon Min; Wen-tau Yih; Pang Wei Koh; Luke Zettlemoyer (CoRR, 2025)

WaterDrum: Watermarking for Data-centric Unlearning Metric. - Xinyang Lu; Xinyuan Niu 001; Gregory Kang Ruey Lau; Bui Thi Cam Nhung; Rachael Hwee Ling Sim; Fanyu Wen; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2025)

ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment. - Xiaoqiang Lin; Arun Verma; Zhongxiang Dai; Daniela Rus; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2025)

COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents. - Arun Verma; Indrajit Saha; Makoto Yokoo; Bryan Kian Hsiang Low (CoRR, 2025)

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs. - Ze Yu Zhang; Bolin Ding; Bryan Kian Hsiang Low (CoRR, 2025)

Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation. - Ze Yu Zhang; Zitao Li; Yaliang Li; Bolin Ding; Bryan Kian Hsiang Low (CoRR, 2025)

MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents. - Zijian Zhou; Ao Qu; Zhaoxuan Wu; Sunghwan Kim; Alok Prakash; Daniela Rus; Jinhua Zhao; Bryan Kian Hsiang Low; Paul Pu Liang (CoRR, 2025)