#### Filter Results:

- Full text PDF available (9)

#### Publication Year

2009

2016

- This year (0)
- Last 5 years (4)
- Last 10 years (9)

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen
- Journal of Machine Learning Research
- 2010

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)

- Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen, Yoshikazu Nagai
- The 2012 International Joint Conference on Neural…
- 2012

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)

- Mikael Kuusela, Tapani Raiko, Antti Honkela, Juha Karhunen
- 2009 International Joint Conference on Neural…
- 2009

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)

- Mikael Kuusela, Tapani Raiko, Harri Ehtamo, TkT Tapani Raiko
- 2009

Avainsanat: koneoppiminen, bayesilainen päättely, variaatio-Bayes-oppiminen, informaatiogeometria, luonnollinen konjugaattigradientti, Gaussin mikstuuri helsinki university of technology abstract of the bachelor's thesis It is a typical problem in machine learning that one wants to represent a given set of data using some parametric model. The parameters of… (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)

- Mikael Kuusela, Epfl Prof Smat, Victor Panaretos, Epfl Smat
- 2014

We study a semi-supervised anomaly detection problem where anomalies lie among the normal data. Instead of analyzing individual observations, anomalies are identified collectively based on deviations from the distribution of the normal data. We first model the normal data using a mixture of Gaussians and then use a variant of the EM algorithm to fit a… (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)

PAR Mikael Johan KUUSELA " If everybody tells you it's possible, then you are not dreaming big enough. " — Bertrand Piccard and André Borschberg, while crossing the Pacific Ocean on a solar-powered aircraft To my parents Acknowledgements I would first and foremost like to sincerely thank my advisor Victor Panaretos for the opportunity of carrying out this… (More)

- ‹
- 1
- ›