From WWWwikiEn<span style="display: none;">[[Image:Fusion11_Krauthausen]][[Image:Fusion11_Ruoff]]</span> <span style="display: none;">[[Image:Fusion11_Krauthausen]][[Image:Fusion11_Ruoff]]</span>

Patrick Ruoff

	Dipl.-Phys. Research Assistant, Ph.D. Student
	Address:	Karlsruher Institut für Technologie Institut für Anthropomatik Gebäude 50.20 Raum 134 Adenauerring 2 D-76131 Karlsruhe
	Walk-in hours:	on arrangement
	Phone:	+49-721-608-45292
	E-mail:	Patrick.Ruoff@kit.edu

Academic Career

Research Interests

• Entropy
• Density approximation and filtering by means of Dirac mixtures

Teaching

• Assistant and coordinator for the lecture Stochastische Informationsverarbeitung (Probabilistic Information Processing) (winter term 2011/2012),
• Supervision of small groups of students in the Praktikum: Forschungsprojekt "Intelligente Sensor-Aktor-Systeme" laboratory (winter term 2010/2011, summer term 2011).

Publications

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Florian Faion, Patrick Ruoff, Antonio Zea, Uwe D. Hanebeck
Title : Recursive Bayesian Calibration of RGBD-Cameras with Non-Overlapping Views (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Peter Krauthausen, Patrick Ruoff, Uwe D. Hanebeck
Title : Sparse Mixture Conditional Density Estimation by Superficial Regularization
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

In this paper, the estimation of conditional densities between continuous random variables from noisy samples is considered.
The conditional densities are modeled as heteroscedastic Gaussian mixture densities allowing for closed-form solution of Bayesian inference with full-densities.
The main contributions of this paper are an improved generalization quality of the estimates by the introduction of a superficial regularizer, the consideration
of model uncertainty relative to local data densities by means of adaptive covariances, and the proposition of an efficient distance-based estimation algorithm.
This algorithm corresponds to an iterative nested optimization scheme, optimizing hyper-parameters, component placement, and mixture weights.
The obtained solutions are sparse, smooth, and generalize well as benchmark experiments, e.g., in nonlinear filtering show.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Patrick Ruoff, Peter Krauthausen,, Uwe D. Hanebeck
Title : Progressive Correction for Deterministic Dirac Mixture Approximations
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Date : July 2011

Abstract

Since the advent of Monte-Carlo particle filtering, particle representations of densities have become increasingly popular due to their flexibility and implicit adaptive resolution.
In this paper, an algorithm for the multiplication of a systematic Dirac mixture (DM) approximation with a continuous likelihood function is presented,
which applies a progressive correction scheme, in order to avoid the particle degeneration problem.
The preservation of sample regularity and therefore, representation quality of the underlying smooth density, is ensured by including
a new measure of smoothness for Dirac mixtures, the DM energy, into the distance measure.
A comparison to common correction schemes in Monte-Carlo methods reveals large improvements especially in cases of small overlap between the likelihood and prior density,
as well as for multi-modal likelihoods.