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Winning the NIST Contest: A scalable and general approach to differentially private synthetic data

Ryan McKenna, Gerome Miklau, Daniel Sheldon
2021 Journal of Privacy and Confidentiality  
We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.  ...  We present two mechanisms, NIST-MST and MST, that are instances of this general approach.  ...  Ifrim, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I, volume 11051 of Lecture Notes  ... 
doi:10.29012/jpc.778 fatcat:b2s37gulojbxxm2buyrfzw7vq4

Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation [article]

Guillaume Salha-Galvan
2022 arXiv   pre-print
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based machine learning problems  ...  In the last part of this thesis, we evaluate our methods on several graphs extracted from the music streaming service Deezer. We put the emphasis on graph-based music recommendation problems.  ...  Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020) [310] .  ... 
arXiv:2205.14651v1 fatcat:rm7mktndtfbm3i5qigqmm24qsu