Mikael Kuusela

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Variational Bayesian (VB) methods are typically only applied to models in the conjugate-exponential family using the variational Bayesian expectation maximisation (VB EM) algorithm or one of its variants. In this paper we present an efficient algorithm for applying VB to more general models. The method is based on specifying the functional form of the(More)
While variational Bayesian (VB) inference is typically done with the so called VB EM algorithm, there are models where it cannot be applied because either the E-step or the M-step cannot be solved analytically. In 2007, Honkela et al. introduced a recipe for a gradient-based algorithm for VB inference that does not have such a restriction. In this paper, we(More)
We study a novel type of a semi-supervised anomaly detection problem where the anomalies occur collectively among a background of normal data. Such problem arises in experimental high energy physics when one is trying to discover deviations from known Standard Model physics. We solve the problem by first fitting a mixture of Gaussians to a labeled(More)
Multivariate machine learning techniques provide an alternative to the rapidity gap method for event-by-event identification and classification of diffraction in hadron-hadron collisions. Traditionally, such methods assign each event exclusively to a single class producing classification errors in overlap regions of data space. As an alternative to this so(More)
This thesis studies statistical inference in the high energy physics unfolding problem, which is an ill-posed inverse problem arising in data analysis at the Large Hadron Collider (LHC) at CERN. Any measurement made at the LHC is smeared by the finite resolution of the particle detectors and the goal in unfolding is to use these smeared measurements to make(More)
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially useful when applying variational Bayes (VB) to models outside the conjugate-exponential family. For them, variational Bayesian expectation maximization (VB EM) algorithms are not(More)
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen and Yoshikazu Nagai Name of the publication Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection Publisher School of Science Unit Department of Information and Computer Science Series Aalto University(More)
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