Research
My research is mainly about local pattern discovery connects explainable subgroups in the datasets to the machine learning models, which we call Exceptional Model Mining (EMM). My study values the importance of data and the functions learned from those data as certain representations. Hence, I`m interested in exploring various search algorithms for pattern discovery and machine learning / probabilistic models for knowledge discovery.
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Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du,
Benedicte Legastelois,
Bhargavi Ganesh,
Ajitha Rajan,
Hana Chockler,
Vaishak Belle,
Stuart Anderson,
Subramanian Ramamoorthy
arXiv, 2022
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Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Xin Du,
Subramanian Ramamoorthy,
Wouter Duivesteijn,
Jin Tian,
Mykola Pechenizkiy
arXiv, 2021
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Exceptional Spatio-temporal Behavior Mining Through Bayesian Non-Parametric Modeling
Xin Du,
Yulong Pei,
Wouter Duivesteijn,
Mykola Pechenizkiy
Journal Track of EMCL PKDD, 2020
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Adversarial Balancing-Based Representation Learning For Causal Effect Inference With Observational Data
Xin Du,
Lei Sun,
Wouter Duivesteijn,
Alexander Nikolaev,
Mykola Pechenizkiy
Data Mining and Knowledge Discovery Special Issue: Mining for Health, 2021
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Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data
Xin Du,
Yulong Pei,
Wouter Duivesteijn,
Mykola Pechenizkiy
AAAI Conference on Artificial Intelligence (AAAI), 2020
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