Dörthe Malzahn

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6ranked-venue papers

6as first author

0since 2021 · last 2003

—ORCID · none

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

Artificial intelligence and machine learning · 6 · 6 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

5 papers

Learning theory · 57% Probabilistic and Bayesian machine learning · 40% Kernel, tree and ensemble methods · 3%

Topics — the 7 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes gaussian process	0.1	3	2002	A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages · NIPS 2002 A Variational Approach to Learning Curves · NIPS 2001 Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations · NIPS 2000
Machine learning › Learning theory › generalization error generalization error estimation	0.1	2	2003	Approximate Analytical Bootstrap Averages for Support Vector Classifiers · NIPS 2003 A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages · NIPS 2002
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › gaussian process gaussian process regression	0.1	2	2001	A Variational Approach to Learning Curves · NIPS 2001 Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations · NIPS 2000
Machine learning › Learning theory learning curves	0.1	2	2001	A Variational Approach to Learning Curves · NIPS 2001 Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations · NIPS 2000
Machine learning › Learning theory resampling	0.0	1	2003	An Approximate Analytical Approach to Resampling Averages · J. Mach. Learn. Res. 2003
Machine learning › Learning theory statistical learning theory	0.0	1	2003	An Approximate Analytical Approach to Resampling Averages · J. Mach. Learn. Res. 2003
Machine learning › Kernel, tree and ensemble methods support vector machine	0.0	1	2003	Approximate Analytical Bootstrap Averages for Support Vector Classifiers · NIPS 2003

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

replica method · 0.1monte carlo sampling · 0.1variational method · 0.1asymptotic analysis · 0.0TAP approach · 0.0statistical mechanics · 0.0

Year	Publication	Venue	Position
2003	Approximate Analytical Bootstrap Averages for Support Vector Classifiers abstract We compute approximate analytical bootstrap averages for support vec- tor classiﬁcation using a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling. Dörthe Malzahn, Manfred Opper	NIPS	1
2003	An Approximate Analytical Approach to Resampling Averages Dörthe Malzahn, Manfred Opper	J. Mach. Learn. Res.	1
2002	A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages abstract We apply the replica method of Statistical Physics combined with a vari- ational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our ap- proach on regression with Gaussian processes and compare our results with averages obtained by Monte-Carlo sampling. Dörthe Malzahn, Manfred Opper	NIPS	1
2001	Learning Curves for Gaussian Processes Models: Fluctuations and Universality Dörthe Malzahn, Manfred Opper	ICANN	1
2001	A Variational Approach to Learning Curves abstract We combine the replica approach from statistical physics with a varia- tional approach to analyze learning curves analytically. We apply the method to Gaussian process regression. As a main result we derive ap- proximative relations between empirical error measures, the generaliza- tion error and the posterior variance. Dörthe Malzahn, Manfred Opper	NIPS	1
2000	Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations abstract Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves for Gaus(cid:173) sian process regression models. The approximation works well in the large sample size limit and for arbitrary dimensionality of the input space. We explain how the approximation can be systemati(cid:173) cally improved and argue that similar techniques can be applied to general likelihood models. Dörthe Malzahn, Manfred Opper	NIPS	1