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Brief Biography

I am currently a Research Scientist at CENTRE FOR FRONTIER AI RESEARCH (CFAR) in Agency for Science, Technology and Research (A*STAR). I obtained my Ph.D. degree from the Australian Artificial Intelligence Institute(AAII) in University of Technology Sydney(UTS), supervised by Prof. Ivor W. Tsang. I received my B.Eng degree and M.Eng degree in Computer Science and Technology from South China University of Technology.

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Openning

I am looking for self-motivated students to work with me on cutting-edge research projects. I accept PhD students, research interns, and CSC visiting students supported by grants or scholarships (e.g., A*STAR SINGA Scholarship , A*STAR SIPGA Scholarship and ARAP ).

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My Research

My research interests lie primarily in the area of statistical machine learning and optimization. I am particularly interested in black-box optimization, kernel methods, and approximation theory. I am mainly focusing on the following topics:

• Black-box Optimization and Reinforcement Learning (RL)

• Black-box optimization is a subarea of optimization. It handles the cases that only function quire can be accessed. The potential applications include reinforcement learning (RL), scientific design, and LLM target generation, etc. The goal is to design efficient and theoretically sound black-box optimization algorithms to improve query efficiency. These algorithms can be used for model-free RL. Moreover, the methodology of black-box optimization is also helpful for designing efficient RL methods.

• Quasi-Monte Carlo (QMC) and Kernel Methods

• QMC theory is a fundamental part of approximation theory. The goal of QMC methods is to design good points set for integral approximation. It can be used in Bayesian Inference and kernel approximation, etc. QMC based feature maps are a promising direction of random feature methods, which can reduce the time and space complexity of kernel methods (e.g., Gaussian Process and SVM). Interestingly, there is a close relationship between random feature maps and neural networks. Besides, QMC methods also have applications in sampling techniques in RL and generative models.

• Robust Learning and Weakly-supervised Learning

• Both robust learning and weakly-supervised learning are subareas of machine learning. Robust learning and weakly-supervised learning aims at addressing a more practical situation in real-life. The input collection may contain noise; the annotation may be corrupted or even expensive to acquire. It is challenging to develop smart algorithms to handle these cases.

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Selected Recent Publications

• Black-box Optimization and RL:

• Yueming Lyu, Atsushi Nitanda, Ivor W. Tsang. Nonparametric Distributional Black-box Optimization via Diffusion Process. In ICLR 2025 DeLTa Workshop , 2025

• Yueming Lyu, Kim Yong Tan, Yew-Soon Ong, Ivor W. Tsang. Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation. Preprint

• Kim Yong Tan, Yueming Lyu^#, Ivor W. Tsang, Yew-Soon Ong. Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation. In International Conference on Learning Representations (ICLR), 2025

• Feiyang Ye^*, Yueming Lyu^* , Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang. Sharpness-Aware Black-Box Optimization. In International Conference on Learning Representations (ICLR), 2025

• Feiyang Ye^*, Yueming Lyu^* , Xuehao Wang, Yu Zhang, Ivor W. Tsang. Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning. In International Conference on Learning Representations (ICLR), 2024

• Yueming Lyu. Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization. In Conference on Neural Information Processing Systems (NeurIPS), 2023

• Jing Li, Yuangang Pan, Yueming Lyu, Yinghua Yao, Yulei Sui, Ivor W. Tsang Earning Extra Performance from Restrictive Feedbacks. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

• Yueming Lyu and Ivor W. Tsang. Black-box Optimizer with Implicit Natural Gradient. In European Conference on Machine Learning. (ECML), 2021

• Yueming Lyu, Yuan Yuan and Ivor W. Tsang. Efficient Batch Black-box Optimization with Deterministic Regret Bounds. Preprint.

• Xingrui Yu, Yueming Lyu and Ivor W. Tsang. Intrinsic Reward Driven Imitation Learning via Generative Model. In International Conference on Machine Learning (ICML), 2020

• QMC and Kernel Methods:

• Yueming Lyu and Ivor W. Tsang. Neural Optimization Kernel: Towards Robust Deep Learning. Preprint.

• Yueming Lyu, Yuan Yuan and Ivor W. Tsang. Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. Conference on Neural Information Processing Systems (NeurIPS), 2020

• Yueming Lyu. Spherical structured feature maps for kernel approximation. In International Conference on Machine Learning (ICML), 2017

• Robust Learning and Weakly-supervised Learning:

• Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor Tsang, Yanfeng Wang. Diversified Batch Selection for Training Acceleration. In International Conference on Machine Learning (ICML), 2024

• Feng Hong, Jiangchao Yao, Yueming Lyu, Zhihan Zhou, Ivor Tsang, Ya Zhang, Yanfeng Wang. On Harmonizing Implicit Subpopulations. In International Conference on Learning Representations (ICLR), 2024

• Yueming Lyu and Ivor W. Tsang. Curriculum Loss: Robust Learning and Generalization against Label Corruption. In International Conference on Learning Representations (ICLR), 2020

• Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-Yan Yeung. Marginalized average attentional network for weakly-supervised learning. In International Conference on Learning Representations (ICLR), 2019

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Academic Service

• Conference Reviewer/PC Member:

• ICML, NeurIPS, ICLR, AISTATS, AAAI, IJCAI, ACML

• Journal Reviewer:

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

• Journal of Artificial Intelligence Research

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

• Machine Learning