2,768 research outputs found
Embarrassingly Shallow Autoencoders for Sparse Data
Combining simple elements from the literature, we define a linear model that
is geared toward sparse data, in particular implicit feedback data for
recommender systems. We show that its training objective has a closed-form
solution, and discuss the resulting conceptual insights. Surprisingly, this
simple model achieves better ranking accuracy than various state-of-the-art
collaborative-filtering approaches, including deep non-linear models, on most
of the publicly available data-sets used in our experiments.Comment: In the proceedings of the Web Conference (WWW) 2019 (7 pages
Encrypted statistical machine learning: new privacy preserving methods
We present two new statistical machine learning methods designed to learn on
fully homomorphic encrypted (FHE) data. The introduction of FHE schemes
following Gentry (2009) opens up the prospect of privacy preserving statistical
machine learning analysis and modelling of encrypted data without compromising
security constraints. We propose tailored algorithms for applying extremely
random forests, involving a new cryptographic stochastic fraction estimator,
and na\"{i}ve Bayes, involving a semi-parametric model for the class decision
boundary, and show how they can be used to learn and predict from encrypted
data. We demonstrate that these techniques perform competitively on a variety
of classification data sets and provide detailed information about the
computational practicalities of these and other FHE methods.Comment: 39 page
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