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Found 9 papers, 2 papers with code
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection
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 Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets.
Differentially Private Markov Chain Monte Carlo
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects.
Differentially Private Variational Inference for Non-conjugate Models
It is built on top of doubly stochastic variational inference, a recent advance which provides a variational solution to a large class of models.
Efficient differentially private learning improves drug sensitivity prediction
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.
Learning Mixtures of Ising Models using Pseudolikelihood
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models.
Learning the Information Divergence
Here we present a framework that facilitates automatic selection of the best divergence among a given family, based on standard maximum likelihood estimation.
Clustering by Nonnegative Matrix Factorization Using Graph Random Walk
Nonnegative Matrix Factorization (NMF) is a promising relaxation technique for clustering analysis.
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation
In this paper we describe a maximum likelihood likelihood approach for dictionary learning in the multiplicative exponential noise model.
