Bryan Kian Hsiang Low

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 127

Top Venues

Publications

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 (2024)
Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
Prompt Optimization with Human Feedback. CoRR (2024)
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 (2024)
Zhuanghua Liu, Bryan Kian Hsiang Low
Decentralized Sum-of-Nonconvex Optimization. AAAI (2024)
Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
Robustifying and Boosting Training-Free Neural Architecture Search. CoRR (2024)
Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet
Optimistic Bayesian Optimization with Unknown Constraints. ICLR (2024)
Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. AAAI (2024)
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)
Zhuanghua Liu, Bryan Kian Hsiang Low
Decentralized Sum-of-Nonconvex Optimization. CoRR (2024)
Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
PINNACLE: PINN Adaptive ColLocation and Experimental points selection. ICLR (2024)
Rui Qiao, Bryan Kian Hsiang Low
Understanding Domain Generalization: A Noise Robustness Perspective. ICLR (2024)
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. ICLR (2024)
Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, Bryan Kian Hsiang Low
Incentive-Aware Federated Learning with Training-Time Model Rewards. ICLR (2024)
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 (2024)
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 (2024)
Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
Neural Dueling Bandits. CoRR (2024)
Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
Robustifying and Boosting Training-Free Neural Architecture Search. ICLR (2024)
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 (2024)
Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
Incentives in Private Collaborative Machine Learning. CoRR (2024)
Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
PINNACLE: PINN Adaptive ColLocation and Experimental points selection. CoRR (2024)
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 (2024)
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR (2024)
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. CoRR (2024)
Rui Qiao, Bryan Kian Hsiang Low
Understanding Domain Generalization: A Noise Robustness Perspective. CoRR (2024)
Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan Kian Hsiang Low
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs. CoRR (2024)
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI (2024)
Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning. CoRR (2023)
Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. CoRR (2023)
Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low
Fair yet Asymptotically Equal Collaborative Learning. CoRR (2023)
Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
Incentives in Private Collaborative Machine Learning. NeurIPS (2023)
Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. CoRR (2023)
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)
Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. ICLR (2023)
Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. NeurIPS (2023)
Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low
Hessian-Aware Bayesian Optimization for Decision Making Systems. CoRR (2023)
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 (2023)
Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low
Bayesian Optimization with Cost-varying Variable Subsets. NeurIPS (2023)
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)
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 (2023)
Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low
Fair yet Asymptotically Equal Collaborative Learning. ICML (2023)
Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low
Pruning during training by network efficacy modeling. Mach. Learn. 112 (7) (2023)
Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. ICML (2023)
Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
Quantum Bayesian Optimization. CoRR (2023)
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)
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low
FedHQL: Federated Heterogeneous Q-Learning. AAMAS (2023)
Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
Quantum Bayesian Optimization. NeurIPS (2023)
Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low
Collaborative Causal Inference with Fair Incentives. ICML (2023)
Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low
Model Shapley: Equitable Model Valuation with Black-box Access. NeurIPS (2023)
Tiedong Liu, Bryan Kian Hsiang Low
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks. CoRR (2023)
Yizhou Chen, Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Teck-Hua Ho
Recursive reasoning-based training-time adversarial machine learning. Artif. Intell. 315 (2023)
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)
Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
Federated Neural Bandits. ICLR (2023)
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer
FedHQL: Federated Heterogeneous Q-Learning. CoRR (2023)
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 (2023)
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)
Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. CoRR (2023)
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)
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. NeurIPS (2022)
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. CoRR (2022)
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Rectified Max-Value Entropy Search for Bayesian Optimization. CoRR (2022)
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. NeurIPS (2022)
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)
Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
Bayesian Optimization under Stochastic Delayed Feedback. CoRR (2022)
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 (2022)
Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
Neural ensemble search via Bayesian sampling. UAI (2022)
Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. CoRR (2022)
Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
Federated Neural Bandit. CoRR (2022)
Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low
DAVINZ: Data Valuation using Deep Neural Networks at Initialization. ICML (2022)
Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
On Provably Robust Meta-Bayesian Optimization. CoRR (2022)
Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
On provably robust meta-Bayesian optimization. UAI (2022)
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR (2022)
Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
Bayesian Optimization under Stochastic Delayed Feedback. ICML (2022)
Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. ICML (2022)
Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low
Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges. IJCAI (2022)
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS (2022)
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 (2022)
Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. ICML (2022)
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)
Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. AAAI (2022)
Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. CoRR (2021)
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Learning to learn with Gaussian processes. UAI (2021)
Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Validation Free and Replication Robust Volume-based Data Valuation. NeurIPS (2021)
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet
Collaborative Bayesian Optimization with Fair Regret. ICML (2021)
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Value-at-Risk Optimization with Gaussian Processes. ICML (2021)
Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet
Top-k Ranking Bayesian Optimization. AAAI (2021)
Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. CoRR (2021)
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
An Information-Theoretic Framework for Unifying Active Learning Problems. AAAI (2021)
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)
Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
Model Fusion for Personalized Learning. ICML (2021)
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. CoRR (2021)
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)
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet
Trusted-maximizers entropy search for efficient Bayesian optimization. UAI (2021)
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS (2021)
Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet
Convolutional Normalizing Flows for Deep Gaussian Processes. IJCNN (2021)
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. CoRR (2021)
Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet
Convolutional Normalizing Flows for Deep Gaussian Processes. CoRR (2021)
Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search. CoRR (2021)
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Value-at-Risk Optimization with Gaussian Processes. CoRR (2021)
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Optimizing Conditional Value-At-Risk of Black-Box Functions. NeurIPS (2021)
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Bryan Kian Hsiang Low