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Targeted Active Learning for Bayesian Decision-Making
Louis Filstroff, Iiris Sundin, Petrus Mikkola +3
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accu…
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Louis Filstroff, Olivier Gouvert, Cédric Févotte +1
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data. In particular, a lot of effort has been devoted to pro…
Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
Alberto Lumbreras, Louis Filstroff, Cédric Févotte
Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnega…