Reimar Hofmann · EDBT 2026 Demo

EDBT 2026 Demo / reviewers in the wild / expert

Reimar Hofmann

dblp:80/2046 · DBLP profile ↗

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6ranked-venue papers

2as first author

0since 2021 · last 2004

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 3 papers		Probabilistic and Bayesian machine learning · 95% Deep learning architectures and training · 5%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Embedded and real-time systems · 50% Hardware accelerators and domain-specific architectures · 50%

Topics — the 5 heaviest of 6, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models markov random field	0.0	1	1997	Nonlinear Markov Networks for Continuous Variables · NIPS 1997
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models bayesian network	0.0	1	1995	Discovering Structure in Continuous Variables Using Bayesian Networks · NIPS 1995
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models structure learning	0.0	1	1995	Discovering Structure in Continuous Variables Using Bayesian Networks · NIPS 1995
Embedded and real-time systems industrial control systems	0.0	1	1991	Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency · NIPS 1991
Hardware accelerators and domain-specific architectures neural network control	0.0	1	1991	Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency · NIPS 1991

Methods — techniques the papers use, named apart from their topics

markov network · 0.0neural network control · 0.0domain theory integration · 0.0bayesian network structure learning · 0.0

Year	Publication	Venue	Position
2004	Generative binary codes Michael Haft, Reimar Hofmann, Volker Tresp	Pattern Anal. Appl.	2
1999	Mixture Approximations to Bayesian Networks Volker Tresp, Michael Haft, Reimar Hofmann	UAI	3
1998	Nonlinear Time-Series Prediction with Missing and Noisy Data abstract We derive solutions for the problem of missing and noisy data in nonlinear time&hyphenseries prediction from a probabilistic point of view. We discuss different approximations to the solutions &hyphen in particular, approximations that require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly used heuristics can lead to suboptimal solutions. We show how error bars for the predictions can be derived and how our results can be applied to K&hyphenstep prediction. We verify our solutions using two chaotic time series and the sunspot data set. In particular, we show that for K&hyphenstep prediction, stochastic simulation is superior to simply iterating the predictor. Volker Tresp, Reimar Hofmann	Neural Comput.	2
1997	Nonlinear Markov Networks for Continuous Variables Reimar Hofmann, Volker Tresp	NIPS	1
1995	Discovering Structure in Continuous Variables Using Bayesian Networks Reimar Hofmann, Volker Tresp	NIPS	1
1991	Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency Martin Röscheisen, Reimar Hofmann, Volker Tresp	NIPS	2