The Manchester MLO Group conducts world-leading research into a wide
range of techniques and applications of machine learning, optimization,
data mining, probabilistic modelling, pattern recognition and machine
perception. The group spans the field from new theoretical developments
to large applications, and is currently supported by a
number of research bodies, including EPSRC, BBSRC, and several
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Congratulations to MLO members Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock and Gavin Brown,
on their new JMLR paper Beyond
Fano's inequality: bounds on the optimal
F-score, balanced error rate, and cost-sensitive risk using conditional entropy and their
Congratulations to MLO members Joe Mellor and Jon Shapiro on their accepted AISTATS paper
Thompson Sampling in Switching Environments with
Bayesian Online Change
Congratulations to MLO members Hassan Bashir and Richard Neville on two new papers now available on IEEExplore.
A Hybrid Evolutionary Computation Algorithm for
Global Optimization, and Convergence measurement in evolutionary
computation using Price's theorem, which were published in the IEEE Conference on Evolutionary Computation in June.
Congratulations to MLO member Peter Glaus on the publication of a new article
in the Journal of Bioinformatics, titled
Identifying differentially expressed transcripts from RNA-seq data with biological variation.
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deep neural nets,