基本信息
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个人简介
Research Interests
Prof Vazirgiannis has conducted research in the area of large scale distributed data management and mining. Currently his research work and interests are oriented towards:
machine learning for graphs (graph kernels, embedding methods, deep learning for graph classification, large scale community detection) with applications in fraud detection
NLP and text mining (Graph of Words, Deep learning for text classification, summarisation and keyword extraction) applications
decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimization, global optimisation, graph theory. We are interested both in methodology and applications, with a special focus on applications in energy optimization and computational geometry.
event and anomaly detection in data streams and time series (applications in text streams, sensory data, personalised medicine)
structured output prediction (multi-label classification, multi-output and sequential/dynamical models, probabilistic models and neural networks)
reinforcement learning (Bayesian models, and deep learning)
Prof Vazirgiannis has conducted research in the area of large scale distributed data management and mining. Currently his research work and interests are oriented towards:
machine learning for graphs (graph kernels, embedding methods, deep learning for graph classification, large scale community detection) with applications in fraud detection
NLP and text mining (Graph of Words, Deep learning for text classification, summarisation and keyword extraction) applications
decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimization, global optimisation, graph theory. We are interested both in methodology and applications, with a special focus on applications in energy optimization and computational geometry.
event and anomaly detection in data streams and time series (applications in text streams, sensory data, personalised medicine)
structured output prediction (multi-label classification, multi-output and sequential/dynamical models, probabilistic models and neural networks)
reinforcement learning (Bayesian models, and deep learning)
研究兴趣
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CoRR (2024)
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AAAI 2024no. 10 (2024): 10757-10765
AAAI 2024no. 19 (2024): 21063-21071
CoRR (2024)
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CoRR (2024)
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ICLR 2024 (2024)
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Conference on Empirical Methods in Natural Language Processing (2023): 4241-4253
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