基本信息
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个人简介
Research Interests
My research interests lie in statistical hypothesis testing and trustworthy machine learning. Specifically, my current research work center around the following topics:
Statistical Hypothesis Testing:
Two-sample Testing: Testing if two datasets are drawn from the same distribution.
Goodness-of-fit Testing: Testing if data are drawn from a given distribution.
Independence Testing: Testing if two datasets are independent.
Trustworthy Machine Learning:
Defending against Adversarial Attacks: Detecting adversarial attacks (i.e., adversarial attack detection); Training a robust model against future adversarial attacks (i.e., adversarial training).
Being Aware of Out-of-distribution Data: Detecting out-of-distribution data; Training a robust model in the open world (e.g., open-set learning, out-of-distribution generalization).
Learning/Inference under Distribution Shift (a.k.a., Transfer Learning): Leveraging the knowledge from domains with abundant labels (i.e., source domains)/pre-trained models (i.e., source models) to complete classification/clustering tasks in an unlabeled domain (i.e., target domain), where two domains are different but related.
Protecting Data Privacy: Training a model to ensure that the training data will not be obtained by inverting the model (i.e., defending against model-inversion attacks).
My research interests lie in statistical hypothesis testing and trustworthy machine learning. Specifically, my current research work center around the following topics:
Statistical Hypothesis Testing:
Two-sample Testing: Testing if two datasets are drawn from the same distribution.
Goodness-of-fit Testing: Testing if data are drawn from a given distribution.
Independence Testing: Testing if two datasets are independent.
Trustworthy Machine Learning:
Defending against Adversarial Attacks: Detecting adversarial attacks (i.e., adversarial attack detection); Training a robust model against future adversarial attacks (i.e., adversarial training).
Being Aware of Out-of-distribution Data: Detecting out-of-distribution data; Training a robust model in the open world (e.g., open-set learning, out-of-distribution generalization).
Learning/Inference under Distribution Shift (a.k.a., Transfer Learning): Leveraging the knowledge from domains with abundant labels (i.e., source domains)/pre-trained models (i.e., source models) to complete classification/clustering tasks in an unlabeled domain (i.e., target domain), where two domains are different but related.
Protecting Data Privacy: Training a model to ensure that the training data will not be obtained by inverting the model (i.e., defending against model-inversion attacks).
研究兴趣
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Xunye Tian,Feng Liu
DATABASES THEORY AND APPLICATIONS, ADC 2023 (2024): 17-29
EXPERT SYSTEMS WITH APPLICATIONS (2024): 122880
Nature Communicationsno. 1 (2024): 1-14
ICLR 2024 (2024)
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IEEE Transactions on Fuzzy Systemsno. 99 (2024): 1-13
arxiv(2024)
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ICML 2023pp.15067-15088, (2023)
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