no code implementations • 27 Jan 2021 • Ece Calikus, Slawomir Nowaczyk, Mohamed-Rafik Bouguelia, Onur Dikmen
Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary.
no code implementations • 16 Sep 2019 • Ece Calikus, Slawomir Nowaczyk, Anita Sant'Anna, Onur Dikmen
To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets.
1 code implementation • NeurIPS 2019 • Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects.
2 code implementations • 27 Oct 2016 • Joonas Jälkö, Onur Dikmen, Antti Honkela
It is built on top of doubly stochastic variational inference, a recent advance which provides a variational solution to a large class of models.
no code implementations • 7 Jun 2016 • Antti Honkela, Mrinal Das, Arttu Nieminen, Onur Dikmen, Samuel Kaski
Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised.
no code implementations • 8 Jun 2015 • Onur Dikmen
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models.
no code implementations • 5 Jun 2014 • Onur Dikmen, Zhirong Yang, Erkki Oja
Here we present a framework that facilitates automatic selection of the best divergence among a given family, based on standard maximum likelihood estimation.
no code implementations • NeurIPS 2012 • Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja
Nonnegative Matrix Factorization (NMF) is a promising relaxation technique for clustering analysis.
no code implementations • NeurIPS 2011 • Onur Dikmen, Cédric Févotte
In this paper we describe a maximum likelihood likelihood approach for dictionary learning in the multiplicative exponential noise model.