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    Theoretical Foundation of Machine Learning

    This research topic is to study the mathematical models and statistical convergence behavior of machine learning algorithms. For example, the mathematical models for deep neural networks, and statistical analysis of various learning algorithms.

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    Optimization Algorithms

    This research topic is concerned with convex and nonconvex optimization, large scalde and distributed training, automatic tuning of machine learning models and efficient deployment.

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    Robust and Adaptive Algorithms

    This research topic is concerned with the generalization of machine learning procedures to new scenarios, and related robustness issues, such as adversarial examples, noise tolerance, adaptation of ML models to new domains, unsupervised pretraining, and learning with limited resources.

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    Tools and Applications

    This research topic is to develop tools and apply learning algorithms to various applications such as natural language processing, computer vision, and games etc.