Yueming Lyu


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Yueming Lyu

Yueming Lyu

Ph.D. student
Australian Artificial Intelligence Institute (AAII),
University of Technology Sydney (UTS)

E-mail: yueminglyu [at] gmail.com
[Google Scholar]


Brief Biography


My Research

    My research interests lie primarily in the area of statistical machine learning and optimization. I am particularly interested in approximation theory, learning theory, and black-box optimization. I am happy working on developing simple, solid and efficient algorithms for the following problems.
  • 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 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.

  • 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), engineering design and black-box attack, etc. The goal is to design efficient and theoretical sounded 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.

  • 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.


Selected Recent Publications


Professional Service

  • Conference Reviewer/PC Member:

    • ICLR-2021, AISTATS 2021, AAAI-2021

    • NeurIPS-2020, AISTATS 2020, AAAI-2020, ACML-2020

    • ICML-2019, NeurIPS-2019, AISTATS 2019, ACML-2019

  • Journal Reviewer:

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

    • Machine Learning