Zhongxiang Dai

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 58

Top Topics

Gaussian Processes

Machine Learning

Neural Architecture

Monte Carlo Sampling

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)
Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
Robustifying and Boosting Training-Free Neural Architecture Search. CoRR (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)
Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. CoRR (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)
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)
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)
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)
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)
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)
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)
Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. CoRR (2023)
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. CoRR (2022)
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. NeurIPS (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)
Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
Federated Neural Bandit. CoRR (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)
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS (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)
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Value-at-Risk Optimization with Gaussian Processes. ICML (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)
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)
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS (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)
Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
Private Outsourced Bayesian Optimization. ICML (2020)
Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. CoRR (2020)
Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
Federated Bayesian Optimization via Thompson Sampling. CoRR (2020)
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Federated Bayesian Optimization via Thompson Sampling. NeurIPS (2020)
Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. ICML (2020)
Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
Private Outsourced Bayesian Optimization. CoRR (2020)
Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low
Bayesian Optimization with Binary Auxiliary Information. CoRR (2019)
Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
Bayesian Optimization Meets Bayesian Optimal Stopping. ICML (2019)
Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai
Implicit Posterior Variational Inference for Deep Gaussian Processes. NeurIPS (2019)
Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
Bayesian Optimization with Binary Auxiliary Information. UAI (2019)
Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
Implicit Posterior Variational Inference for Deep Gaussian Processes. CoRR (2019)
Jingwen Chai, Gong Chen, Pavithra Thangavel, Georgios N. Dimitrakopoulos, Ioannis Kakkos, Yu Sun, Zhongxiang Dai, Haoyong Yu, Nitish V. Thakor, Anastasios Bezerianos, Junhua Li
Identification of gait-related brain activity using electroencephalographic signals. NER (2017)
Bing Liang Chua, Zhongxiang Dai, Nitish V. Thakor, Anastasios Bezerianos, Yu Sun
Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation. EMBC (2017)
Yu Sun, Julian Lim, Zhongxiang Dai, Kianfoong Wong, Fumihiko Taya, Yu Chen, Junhua Li, Nitish V. Thakor, Anastasios Bezerianos
The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study. NeuroImage 152 (2017)
Zhongxiang Dai, José C. Príncipe, Anastasios Bezerianos, Nitish V. Thakor
Cognitive Workload Discrimination in Flight Simulation Task Using a Generalized Measure of Association. ICONIP (3) (2015)