Naila Murray - Interestingness predictions and getting to grips with data privacy

Underrated ML

This week we are joined by Naila Murray. Naila obtained a B.Sc. in Electrical Engineering from Princeton University in 2007. In 2012, she received her PhD from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined NAVER LABS Europe (then Xerox Research Centre Europe) in January 2013, working on topics including fine-grained visual categorization, image retrieval, and visual attention. From 2015 to 2019 she led the computer vision team at NLE. She currently serves as NLE's director of science. She serves/served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and programme chair for ICLR 2021. Her research interests include representation learning and multi-modal search.
We discuss using sparse pairwise comparisons to learn a ranking function that is robust to outliers. We also take a look at using generative models in order to utilise once inaccessible datasets.
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Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8
Links to the papers:

"Interestingness Prediction by Robust Learning to Rank" [paper]

"Generative Models for Effective ML on Private Decentralized datasets" - [paper]