Low, Bryan Kian Hsiang

121 publications

ICLR 2026 ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment Xiaoqiang Lin, Arun Verma, Zhongxiang Dai, Daniela Rus, See-Kiong Ng, Bryan Kian Hsiang Low

ICLR 2026 DUET: Optimizing LLM Training Data Mixtures via Noisy Feedback from Unseen, Downstream Evaluation Tasks Zhiliang Chen, Gregory Kang Ruey Lau, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICLR 2026 How to Cure Newton for Unlearning Neural Networks? an Empirical Study from the Hessian Perspective Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim, See-Kiong Ng, Bryan Kian Hsiang Low

ICLR 2026 INO-SGD: Addressing Utility Imbalance Under Individualized Differential Privacy Xiao Tian, Jue Fan, Rachael Hwee Ling Sim, Bryan Kian Hsiang Low

ICLR 2026 MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low, Paul Pu Liang

ICLR 2026 TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low

ICLR 2026 WaterDrum: Watermark-Based Data-Centric Unlearning Metric Xinyang Lu, Xinyuan Niu, Gregory Kang Ruey Lau, Nhung Bui, Rachael Hwee Ling Sim, John Russell Himawan, Fanyu Wen, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low

ICLRW 2025 Active Human Feedback Collection via Neural Contextual Dueling Bandits Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low

JMLR 2025 Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng

ICML 2025 BILBO: BILevel Bayesian Optimization Ruth Wan Theng Chew, Quoc Phong Nguyen, Bryan Kian Hsiang Low

ICLRW 2025 Broaden Your SCOPE! Efficient Conversation Planning for LLMs Using Semantic Space Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICLR 2025 Broaden Your SCOPE! Efficient Multi-Turn Conversation Planning for LLMs with Semantic Space Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICLRW 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

ICLR 2025 Efficient Top-M Data Values Identification for Data Selection Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Bryan Kian Hsiang Low

ICML 2025 Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Yu

ICLR 2025 Group-Robust Sample Reweighting for Subpopulation Shifts via Influence Functions Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh, Bryan Kian Hsiang Low

NeurIPS 2025 Incentivizing Time-Aware Fairness in Data Sharing Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Arun Verma, Zhaoxuan Wu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICML 2025 NICE Data Selection for Instruction Tuning in LLMs with Non-Differentiable Evaluation Metric Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Pang Wei Koh, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICLRW 2025 NICE: Non-Differentiable Evaluation Metric-Based Data Selection for Instruction Tuning Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Pang Wei Koh, Chuan-Sheng Foo, 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

ICLRW 2025 OPPA: OPtimizing PArallelism for Language Model Training Apivich Hemachandra, Yizhan Han, See-Kiong Ng, 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

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

ICLRW 2025 SCOPE: Improving LLM Conversations with Efficient Semantic Space Planning Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, Bryan Kian Hsiang Low

ICLRW 2025 Uncertainty Quantification for MLLMs Gregory Kang Ruey Lau, Hieu Dao, Bryan Kian Hsiang Low

ICLRW 2025 Understanding the Relationship Between Prompts and Response Uncertainty in Large Language Models Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low

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

NeurIPS 2024 Active Set Ordering Quoc Phong Nguyen, Sunil Gupta, Svetha Venkatesh, Bryan Kian Hsiang Low, Patrick Jaillet

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

ICMLW 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 Data Distribution Valuation Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti

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

AAAI 2024 Decentralized Sum-of-Nonconvex Optimization Zhuanghua Liu, 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

TMLR 2024 Dependency Structure Search Bayesian Optimization for Decision Making Models Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low

NeurIPSW 2024 Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning Tasks Gregory Kang Ruey Lau, Wenyang Hu, Liu Diwen, Chen Jizhuo, See-Kiong Ng, Bryan Kian Hsiang Low

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

NeurIPSW 2024 Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Yu

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

ICML 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

ICMLW 2024 Heterogeneous Federated Zeroth-Order Optimization Using Gradient Surrogates Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, 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

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

NeurIPS 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

ICMLW 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

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

ICMLW 2024 Neural Dueling Bandits Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, 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

ICMLW 2024 PIED: Physics-Informed Experimental Design for Inverse Problems Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng, 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

ICMLW 2024 PINNACLE: PINN Adaptive ColLocation and Experimental Points Selection Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low

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

ICMLW 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

ICMLW 2024 Prompt Optimization with Human Feedback Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low

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

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

ICLR 2024 Understanding Domain Generalization: A Noise Robustness Perspective Rui Qiao, 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

ICMLW 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

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

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 Bayesian Optimization with Cost-Varying Variable Subsets Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low

ICML 2023 Collaborative Causal Inference with Fair Incentives Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low

NeurIPS 2023 Exploiting Correlated Auxiliary Feedback in Parameterized Bandits Arun Verma, Zhongxiang Dai, Yao Shu, 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

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

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

NeurIPS 2023 Incentives in Private Collaborative Machine Learning Rachael Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet

NeurIPS 2023 Model Shapley: Equitable Model Valuation with Black-Box Access Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low

AISTATS 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

NeurIPSW 2023 PINNACLE: PINN Adaptive ColLocation and Experimental Points Selection Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low

AAAI 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

MLJ 2023 Pruning During Training by Network Efficacy Modeling Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low

NeurIPS 2023 Quantum Bayesian Optimization Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, 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

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

NeurIPSW 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

ICLR 2023 Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, 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

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

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

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

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

UAI 2022 Neural Ensemble Search via Bayesian Sampling Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low

UAI 2022 On Provably Robust Meta-Bayesian Optimization Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

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

NeurIPS 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

AAAI 2021 An Information-Theoretic Framework for Unifying Active Learning Problems Quoc Phong Nguyen, 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

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

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

UAI 2021 Learning to Learn with Gaussian Processes Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

ICML 2021 Model Fusion for Personalized Learning Thanh Chi Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet

NeurIPS 2021 Optimizing Conditional Value-at-Risk of Black-Box Functions Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

AAAI 2021 Top-K Ranking Bayesian Optimization Quoc Phong Nguyen, Sebastian Tay, 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

NeurIPS 2021 Validation Free and Replication Robust Volume-Based Data Valuation Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low

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

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

NeurIPS 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

ICML 2020 Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet

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

ICML 2020 Private Outsourced Bayesian Optimization Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low

ICML 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

AAAI 2020 Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low

NeurIPS 2020 Variational Bayesian Unlearning Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

ICML 2019 Bayesian Optimization Meets Bayesian Optimal Stopping Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

UAI 2019 Bayesian Optimization with Binary Auxiliary Information Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low

ICML 2019 Collective Model Fusion for Multiple Black-Box Experts Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford

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

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

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

ICML 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 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

NeurIPS 2015 Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

ICML 2014 Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli