Zhongxiang Dai

91 papers A* 27A 4Journal 55Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models. CoRR Rui Jia, Ruiyi Lan, Fengrui Liu, Zhongxiang Dai, Bo Jiang, Jing Shao, Jingyuan Chen, Guandong Xu, Fei Wu, Min Zhang
2026	A*	`conf`	Federated Linear Dueling Bandits. AAAI Xuhan Huang, Yan Hu, Zhiyan Li, Zhiyong Wang, Zhongxiang Dai
2026	J	`jnl`	MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks. CoRR Zhi Hong, Qian Zhang, Jiahang Sun, Zhiwei Shang, Mingze Kong, Xiangyi Wang, Yao Shu, Zhongxiang Dai
2026	J	`jnl`	Real-Time Aligned Reward Model beyond Semantics. CoRR Zixuan Huang, Xin Xia, Yuxi Ren, Jianbin Zheng, Xuefeng Xiao, Hongyan Xie, Li Huaqiu, Songshi Liang, Zhongxiang Dai, Fuzhen Zhuang, Jianxin Li, Yikun Ban, Deqing Wang
2026	J	`jnl`	Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation. CoRR Chenxing Wei, Hong Wang, Ying He, Zhongxiang Dai, Bo Jiang, F. Richard Yu, Yao Shu
2026	J	`jnl`	Workflow-R1: Group Sub-sequence Policy Optimization for Multi-turn Workflow Construction. CoRR Mingze Kong, Zikun Qu, Zhongquan Zhou, Pengyu Liang, Xiang Li, Zhiwei Shang, Zhi Hong, Kaiyu Huang, Zhiyong Wang, Zhongxiang Dai
2025	J	`jnl`	Active Human Feedback Collection via Neural Contextual Dueling Bandits. CoRR Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low
2025	J	`jnl`	ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment. CoRR Xiaoqiang Lin, Arun Verma, Zhongxiang Dai, Daniela Rus, See-Kiong Ng, Bryan Kian Hsiang Low
2025	J	`jnl`	Adaptive Sample Scheduling for Direct Preference Optimization. CoRR Zixuan Huang, Yikun Ban, Lean Fu, Xiaojie Li, Zhongxiang Dai, Jianxin Li, Deqing Wang
2025	J	`jnl`	Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization. J. Mach. Learn. Res. Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng
2025	J	`jnl`	Allocation of geometrical errors for developing precision measurement machine. J. Intell. Manuf. Tao Lai, Junfeng Liu, Fulei Chen, Zelong Li, Chaoliang Guan, Huang Li, Chao Xu, Hao Hu, Yifan Dai, Shanyong Chen, Zhongxiang Dai
2025	J	`jnl`	Convergence Rates of Constrained Expected Improvement. CoRR Haowei Wang, Jingyi Wang, Zhongxiang Dai, Naiyuan Chiang, Szu Hui Ng, Cosmin G. Petra
2025	J	`jnl`	Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts. CoRR Zhongjian Qiao, Rui Yang, Jiafei Lyu, Xiu Li, Zhongxiang Dai, Zhuoran Yang, Siyang Gao, Shuang Qiu
2025	J	`jnl`	EduAgentQG: A Multi-Agent Workflow Framework for Personalized Question Generation. CoRR Rui Jia, Min Zhang, Fengrui Liu, Bo Jiang, Kun Kuang, Zhongxiang Dai
2025	J	`jnl`	EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus. CoRR Shou'ang Wei, Min Zhang, Xin Lin, Bo Jiang, Zhongxiang Dai, Kun Kuang
2025	J	`jnl`	FedPOB: Sample-Efficient Federated Prompt Optimization via Bandits. CoRR Pingchen Lu, Zhi Hong, Zhiwei Shang, Zhiyong Wang, Yikun Ban, Yao Shu, Min Zhang, Shuang Qiu, Zhongxiang Dai
2025	J	`jnl`	Federated Linear Dueling Bandits. CoRR Xuhan Huang, Yan Hu, Zhiyan Li, Zhiyong Wang, Benyou Wang, Zhongxiang Dai
2025	J	`jnl`	Large Language Model-Enhanced Multi-Armed Bandits. CoRR Jiahang Sun, Zhiyong Wang, Runhan Yang, Chenjun Xiao, John C. S. Lui, Zhongxiang Dai
2025	J	`jnl`	Meta-Prompt Optimization for LLM-Based Sequential Decision Making. CoRR Mingze Kong, Zhiyong Wang, Yao Shu, Zhongxiang Dai
2025	A*	`conf`	Neural Dueling Bandits: Preference-Based Optimization with Human Feedback. ICLR Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
2025	A*	`conf`	Online Clustering of Dueling Bandits. ICML Zhiyong Wang, Jiahang Sun, Mingze Kong, Jize Xie, Qinghua Hu, John C. S. Lui, Zhongxiang Dai
2025	J	`jnl`	Online Clustering of Dueling Bandits. CoRR Zhiyong Wang, Jiahang Sun, Mingze Kong, Jize Xie, Qinghua Hu, John C. S. Lui, Zhongxiang Dai
2025	A*	`conf`	Refining Adaptive Zeroth-Order Optimization at Ease. ICML Yao Shu, Qixin Zhang, Kun He, Zhongxiang Dai
2025	J	`jnl`	Refining Adaptive Zeroth-Order Optimization at Ease. CoRR Yao Shu, Qixin Zhang, Kun He, Zhongxiang Dai
2025	J	`jnl`	Self-Reflective Generation at Test Time. CoRR Jian Mu, Qixin Zhang, Zhiyong Wang, Menglin Yang, Shuang Qiu, Chengwei Qin, Zhongxiang Dai, Yao Shu
2025	J	`jnl`	T-POP: Test-Time Personalization with Online Preference Feedback. CoRR Zikun Qu, Min Zhang, Mingze Kong, Xiang Li, Zhiwei Shang, Zhiyong Wang, Yikun Ban, Shuang Qiu, Yao Shu, Zhongxiang Dai
2025	J	`jnl`	UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models. CoRR Shou'ang Wei, Min Zhang, Xin Lin, Bo Jiang, Kun Kuang, Zhongxiang Dai
2025	—	`conf`	WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. ACL (Findings) Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Data-Centric AI in the Age of Large Language Models. CoRR 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
2024	A*	`conf`	Localized Zeroth-Order Prompt Optimization. NeurIPS Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Localized Zeroth-Order Prompt Optimization. CoRR Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low
2024	J	`jnl`	Neural Dueling Bandits. CoRR Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
2024	—	`conf`	Position Paper: Data-Centric AI in the Age of Large Language Models. EMNLP (Findings) 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
2024	A*	`conf`	Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. NeurIPS Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. CoRR Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	J	`jnl`	Prompt Optimization with Human Feedback. CoRR Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024	A*	`conf`	Robustifying and Boosting Training-Free Neural Architecture Search. ICLR Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
2024	J	`jnl`	Robustifying and Boosting Training-Free Neural Architecture Search. CoRR Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
2024	A*	`conf`	Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers. ICML Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2023	A*	`conf`	Batch Bayesian Optimization For Replicable Experimental Design. NeurIPS Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Batch Bayesian Optimization for Replicable Experimental Design. CoRR Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet
2023	A*	`conf`	Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. NeurIPS Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
2023	J	`jnl`	Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. CoRR Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low
2023	A	`conf`	FedHQL: Federated Heterogeneous Q-Learning. AAMAS Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low
2023	J	`jnl`	FedHQL: Federated Heterogeneous Q-Learning. CoRR Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer
2023	A*	`conf`	Federated Neural Bandits. ICLR Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients. CoRR Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low
2023	A*	`conf`	Quantum Bayesian Optimization. NeurIPS Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Quantum Bayesian Optimization. CoRR Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2023	J	`jnl`	Recursive reasoning-based training-time adversarial machine learning. Artif. Intell. Yizhou Chen, Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Teck-Hua Ho
2023	A*	`conf`	Training-Free Neural Active Learning with Initialization-Robustness Guarantees. ICML Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
2023	J	`jnl`	Training-Free Neural Active Learning with Initialization-Robustness Guarantees. CoRR Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low
2023	J	`jnl`	Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers. CoRR Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2023	J	`jnl`	WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. CoRR Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
2023	A*	`conf`	Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. ICLR Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low
2022	J	`jnl`	Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization. CoRR Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng
2022	A*	`conf`	Bayesian Optimization under Stochastic Delayed Feedback. ICML Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	J	`jnl`	Bayesian Optimization under Stochastic Delayed Feedback. CoRR Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	J	`jnl`	Federated Neural Bandit. CoRR Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. ICLR Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
2022	A	`conf`	Neural ensemble search via Bayesian sampling. UAI Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
2022	J	`jnl`	On Provably Robust Meta-Bayesian Optimization. CoRR Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A	`conf`	On provably robust meta-Bayesian optimization. UAI Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	J	`jnl`	Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet
2022	A*	`conf`	Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. NeurIPS Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
2022	J	`jnl`	Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. CoRR Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low
2021	A*	`conf`	Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. NeurIPS Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
2021	J	`jnl`	Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. CoRR Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
2021	J	`jnl`	Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search. CoRR Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
2021	J	`jnl`	NASI: Label- and Data-agnostic Neural Architecture Search at Initialization. CoRR Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low
2021	A*	`conf`	Optimizing Conditional Value-At-Risk of Black-Box Functions. NeurIPS Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	A*	`conf`	Value-at-Risk Optimization with Gaussian Processes. ICML Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2021	J	`jnl`	Value-at-Risk Optimization with Gaussian Processes. CoRR Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2020	A*	`conf`	Federated Bayesian Optimization via Thompson Sampling. NeurIPS Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
2020	J	`jnl`	Federated Bayesian Optimization via Thompson Sampling. CoRR Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
2020	A*	`conf`	Private Outsourced Bayesian Optimization. ICML Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
2020	J	`jnl`	Private Outsourced Bayesian Optimization. CoRR Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
2020	A*	`conf`	R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. ICML Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
2020	J	`jnl`	R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. CoRR Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
2019	A*	`conf`	Bayesian Optimization Meets Bayesian Optimal Stopping. ICML Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2019	A	`conf`	Bayesian Optimization with Binary Auxiliary Information. UAI Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
2019	J	`jnl`	Bayesian Optimization with Binary Auxiliary Information. CoRR Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low
2019	A*	`conf`	Implicit Posterior Variational Inference for Deep Gaussian Processes. NeurIPS Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai
2019	J	`jnl`	Implicit Posterior Variational Inference for Deep Gaussian Processes. CoRR Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
2017	—	`conf`	Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation. EMBC Bing Liang Chua, Zhongxiang Dai, Nitish V. Thakor, Anastasios Bezerianos, Yu Sun
2017	—	`conf`	Identification of gait-related brain activity using electroencephalographic signals. NER Jingwen Chai, Gong Chen, Pavithra Thangavel, Georgios N. Dimitrakopoulos, Ioannis Kakkos, Yu Sun, Zhongxiang Dai, Haoyong Yu, Nitish V. Thakor, Anastasios Bezerianos, Junhua Li
2017	J	`jnl`	The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study. NeuroImage Yu Sun, Julian Lim, Zhongxiang Dai, Kianfoong Wong, Fumihiko Taya, Yu Chen, Junhua Li, Nitish V. Thakor, Anastasios Bezerianos
2015	—	`conf`	Cognitive Workload Discrimination in Flight Simulation Task Using a Generalized Measure of Association. ICONIP (3) Zhongxiang Dai, José C. Príncipe, Anastasios Bezerianos, Nitish V. Thakor