Zhongxiang Dai

According to our database, Zhongxiang Dai authored at least 74 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization.

Jian Mu, Tianyi Lin, Chengwei Qin, Zhongxiang Dai, Yao Shu

CoRR, May, 2026

UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling.

Kaiyu Huang, Xingyu Wang, Mingze Kong, Zhubo Shi, Yuqian Hou, Hong Xu, Zhongxiang Dai, Minchen Yu, Qingjiang Shi

CoRR, May, 2026

Linear and Neural Dueling Bandits with Delayed Feedback.

Xiangyi Wang, Pingchen Lu, Jie Mao, Mingze Kong, Zhi Hong, Zhiyong Wang, Zhongxiang Dai

CoRR, May, 2026

ALSO: Adversarial Online Strategy Optimization for Social Agents.

Xiang Li, Liping Yi, Mingze Kong, Min Zhang, Zhongxiang Dai, QingHua Hu

CoRR, May, 2026

Why Zeroth-Order Adaptation May Forget Less: A Randomized Shaping Theory.

Yao Shu, Jian Mu, Zhongxiang Dai

CoRR, May, 2026

Learning to Reason with Insight for Informal Theorem Proving.

Yunhe Li, Hao Shi, Bowen Deng, Wei Wang, Mengzhe Ruan, Hanxu Hou, Zhongxiang Dai, Siyang Gao, Chao Wang, Shuang Qiu, Linqi Song

CoRR, April, 2026

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks.

Zhi Hong, Qian Zhang, Jiahang Sun, Zhiwei Shang, Mingze Kong, Xiangyi Wang, Yao Shu, Zhongxiang Dai

CoRR, March, 2026

Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation.

Chenxing Wei, Hong Wang, Ying He, Zhongxiang Dai, Bo Jiang, F. Richard Yu, Yao Shu

CoRR, March, 2026

CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models.

Rui Jia, Ruiyi Lan, Fengrui Liu, Zhongxiang Dai, Bo Jiang, Jing Shao, Jingyuan Chen, Guandong Xu, Fei Wu, Min Zhang

CoRR, February, 2026

Workflow-R1: Group Sub-sequence Policy Optimization for Multi-turn Workflow Construction.

Mingze Kong, Zikun Qu, Zhongquan Zhou, Pengyu Liang, Xiang Li, Zhiwei Shang, Zhi Hong, Kaiyu Huang, Zhiyong Wang, Zhongxiang Dai

CoRR, February, 2026

Real-Time Aligned Reward Model beyond Semantics.

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

CoRR, January, 2026

Large Language Model-Enhanced Multi-Armed Bandits.

Jiahang Sun, Zhiyong Wang, Runhan Yang, Chenjun Xiao, John C. S. Lui, Zhongxiang Dai

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

Self-Reflective Generation at Test Time.

Jian Mu, Qixin Zhang, Zhiyong Wang, Menglin Yang, Shuang Qiu, Chengwei Qin, Zhongxiang Dai, Yao Shu

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

Federated Linear Dueling Bandits.

Xuhan Huang, Yan Hu, Zhiyan Li, Zhiyong Wang, Zhongxiang Dai

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts.

Zhongjian Qiao, Rui Yang, Jiafei Lyu, Xiu Li, Zhongxiang Dai, Zhuoran Yang, Siyang Gao, Shuang Qiu

CoRR, December, 2025

EduAgentQG: A Multi-Agent Workflow Framework for Personalized Question Generation.

Rui Jia, Min Zhang, Fengrui Liu, Bo Jiang, Kun Kuang, Zhongxiang Dai

CoRR, November, 2025

UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models.

Shou'ang Wei, Min Zhang, Xin Lin, Bo Jiang, Kun Kuang, Zhongxiang Dai

CoRR, November, 2025

EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus.

Shou'ang Wei, Min Zhang, Xin Lin, Bo Jiang, Zhongxiang Dai, Kun Kuang

CoRR, October, 2025

FedPOB: Sample-Efficient Federated Prompt Optimization via Bandits.

Pingchen Lu, Zhi Hong, Zhiwei Shang, Zhiyong Wang, Yikun Ban, Yao Shu, Min Zhang, Shuang Qiu, Zhongxiang Dai

CoRR, September, 2025

T-POP: Test-Time Personalization with Online Preference Feedback.

Zikun Qu, Min Zhang, Mingze Kong, Xiang Li, Zhiwei Shang, Zhiyong Wang, Yikun Ban, Shuang Qiu, Yao Shu, Zhongxiang Dai

CoRR, September, 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., August, 2025

Adaptive Sample Scheduling for Direct Preference Optimization.

Zixuan Huang, Yikun Ban, Lean Fu, Xiaojie Li, Zhongxiang Dai, Jianxin Li, Deqing Wang

CoRR, June, 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, May, 2025

Active Human Feedback Collection via Neural Contextual Dueling Bandits.

Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low

CoRR, April, 2025

Federated Linear Dueling Bandits.

Xuhan Huang, Yan Hu, Zhiyan Li, Zhiyong Wang, Benyou Wang, Zhongxiang Dai

CoRR, February, 2025

Meta-Prompt Optimization for LLM-Based Sequential Decision Making.

Mingze Kong, Zhiyong Wang, Yao Shu, Zhongxiang Dai

CoRR, February, 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

J. Mach. Learn. Res., 2025

Convergence Rates of Constrained Expected Improvement.

Haowei Wang, Jingyi Wang, Zhongxiang Dai, Naiyuan Chiang, Szu Hui Ng, Cosmin G. Petra

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Online Clustering of Dueling Bandits.

Zhiyong Wang, Jiahang Sun, Mingze Kong, Jize Xie, Qinghua Hu, John C. S. Lui, Zhongxiang Dai

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Refining Adaptive Zeroth-Order Optimization at Ease.

Yao Shu, Qixin Zhang, Kun He, Zhongxiang Dai

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Neural Dueling Bandits: Preference-Based Optimization with Human Feedback.

Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data.

Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

Neural Dueling Bandits.

Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low

CoRR, 2024

Data-Centric AI in the Age of Large Language Models.

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

CoRR, 2024

Prompt Optimization with Human Feedback.

Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low

CoRR, 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 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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Robustifying and Boosting Training-Free Neural Architecture Search.

Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Position Paper: Data-Centric AI in the Age of Large Language Models.

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

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

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

Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients.

Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low

CoRR, 2023

FedHQL: Federated Heterogeneous Q-Learning.

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer

CoRR, 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

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits.

Arun Verma, Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Quantum Bayesian Optimization.

Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Training-Free Neural Active Learning with Initialization-Robustness Guarantees.

Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2023

Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation.

Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Neural Bandits.

Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

FedHQL: Federated Heterogeneous Q-Learning.

Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Federated Neural Bandit.

Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet

CoRR, 2022

Neural ensemble search via Bayesian sampling.

Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Uncertainty in Artificial Intelligence, 2022

On provably robust meta-Bayesian optimization.

Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.

Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sample-Then-Optimize Batch Neural Thompson Sampling.

Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Optimization under Stochastic Delayed Feedback.

Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the International Conference on Machine Learning, 2022

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization.

Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, Bryan Kian Hsiang Low

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search.

Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low

CoRR, 2021

Optimizing Conditional Value-At-Risk of Black-Box Functions.

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially Private Federated Bayesian Optimization with Distributed Exploration.

Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Value-at-Risk Optimization with Gaussian Processes.

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Federated Bayesian Optimization via Thompson Sampling.

Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Private Outsourced Bayesian Optimization.

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Bayesian Optimization with Binary Auxiliary Information.

Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Implicit Posterior Variational Inference for Deep Gaussian Processes.

Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Optimization Meets Bayesian Optimal Stopping.

Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 36th International Conference on Machine Learning, 2019

2017

The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study.

Yu Sun, Julian Lim, Zhongxiang Dai, Kianfoong Wong, Fumihiko Taya, Yu Chen, Junhua Li, Nitish V. Thakor, Anastasios Bezerianos

NeuroImage, 2017

Identification of gait-related brain activity using electroencephalographic signals.

Jingwen Chai, Gong Chen, Pavithra Thangavel, Georgios N. Dimitrakopoulos, Ioannis Kakkos, Yu Sun, Zhongxiang Dai, Haoyong Yu, Nitish V. Thakor, Anastasios Bezerianos, Junhua Li

Proceedings of the 8th International IEEE/EMBS Conference on Neural Engineering, 2017

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

Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

2015

Cognitive Workload Discrimination in Flight Simulation Task Using a Generalized Measure of Association.

Zhongxiang Dai, José C. Príncipe, Anastasios Bezerianos, Nitish V. Thakor

Proceedings of the Neural Information Processing - 22nd International Conference, 2015