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Christophe Biernacki

4 accepted papers

2022

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

ICML 2022spotlight

Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information…

2022

Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models

AAAI 2022technical

Domain adaptation aims to leverage source domain knowledge to predict target domain labels. Most domain adaptation methods tackle a single-source, single-target scenario, whereas source and target domain data can often be subdivided into data from different distributions in real-life applications (e…

2017

A mixture model-based real-time audio sources classification method

ICASSP 2017accepted

Recent research on machine learning focuses on audio source identification in complex environments. They rely on extracting features from audio signals and use machine learning techniques to model the sound classes. However, such techniques are often not optimized for a real-time implementation and…

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