My research group investigates the fundamental theory of machine learning. Based on theoretical understanding, we design effective machine learning algorithms for practical problems. We also apply machine learning methods to various applications such as computer vision and natural language processing. Our core machine learning research currently focuses on the following topics.

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    Big Data and System ML

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

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    Robust and Adaptive Algorithms

    This research topic is concerned with the generalization ability of machine learning procedures, adversarial examples, noise tolerance, adaptation of ML models to new scenarios, and learning with limited resources and few examples.

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    Generation Models

    This research topic is concerned with mathematical methods for generative models such as sampling, data generation procedures, and applications such as automated content production, simulation, and data augmentation.

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    Reinforcement Learning

    This research topic is concerned with the theory and algorithms for bandits, MDPs and Game theoretical RL, with applications in multi-agent learning, game AI, and robotic control.