Cognitive Workload Discrimination in Flight Simulation Task Using a Generalized Measure of Association. - Zhongxiang Dai; José C. Príncipe; Anastasios Bezerianos; Nitish V. Thakor (ICONIP (3), 2015)

Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation. - Bing Liang Chua; Zhongxiang Dai; Nitish V. Thakor; Anastasios Bezerianos; Yu Sun 014 (EMBC, 2017)

Identification of gait-related brain activity using electroencephalographic signals. - Jingwen Chai; Gong Chen 001; Pavithra Thangavel; Georgios N. Dimitrakopoulos; Ioannis Kakkos; Yu Sun 014; Zhongxiang Dai; Haoyong Yu; Nitish V. Thakor; Anastasios Bezerianos; Junhua Li (NER, 2017)

The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study. - Yu Sun 014; Julian Lim; Zhongxiang Dai; Kianfoong Wong; Fumihiko Taya; Yu Chen; Junhua Li; Nitish V. Thakor; Anastasios Bezerianos (NeuroImage, 2017)

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

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)

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

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

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

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

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)

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

Sample-Then-Optimize Batch Neural Thompson Sampling. - Zhongxiang Dai; Yao Shu; 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)

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)

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)

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

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)

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. - Arun Verma; Zhongxiang Dai; Yao Shu; 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)

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)

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)

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

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)

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)

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)

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)

Neural Dueling Bandits. - Arun Verma; Zhongxiang Dai; Xiaoqiang Lin; Patrick Jaillet; Bryan Kian Hsiang Low (CoRR, 2024)

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)

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

Meta-Prompt Optimization for LLM-Based Sequential Decision Making. - Mingze Kong; Zhiyong Wang; Yao Shu; Zhongxiang Dai (CoRR, 2025)

Refining Adaptive Zeroth-Order Optimization at Ease. - Yao Shu; Qixin Zhang 001; Kun He 001; Zhongxiang Dai (CoRR, 2025)

Federated Linear Dueling Bandits. - Xuhan Huang; Yan Hu; Zhiyan Li; Zhiyong Wang; Benyou Wang; Zhongxiang Dai (CoRR, 2025)

Large Language Model-Enhanced Multi-Armed Bandits. - Jiahang Sun; Zhiyong Wang; Runhan Yang; Chenjun Xiao; John C. S. Lui; Zhongxiang Dai (CoRR, 2025)

Online Clustering of Dueling Bandits. - Zhiyong Wang; Jiahang Sun; Mingze Kong; Jize Xie; Qinghua Hu; John C. S. Lui; Zhongxiang Dai (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)

Convergence Rates of Constrained Expected Improvement. - Haowei Wang; Jingyi Wang; Zhongxiang Dai; Naiyuan Chiang; Szu Hui Ng; Cosmin G. Petra (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)

Adaptive Sample Scheduling for Direct Preference Optimization. - Zixuan Huang; Yikun Ban; Lean Fu; Xiaojie Li; Zhongxiang Dai; Jianxin Li; Deqing Wang (CoRR, 2025)

Allocation of geometrical errors for developing precision measurement machine. - Tao Lai; Junfeng Liu; Fulei Chen; Zelong Li; Chaoliang Guan; Huang Li; Chao Xu; Hao Hu; Yifan Dai; Shanyong Chen; Zhongxiang Dai (J. Intell. Manuf., 2025)