Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Philipp Batz

dblp:139/1312 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Artificial intelligence and machine learning · 1

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
1 paper
Probabilistic and Bayesian machine learning · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
drift estimation		 0.212013
Approximate Gaussian process inference for the drift function in stochastic differential equations · NIPS 2013
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
expectation-maximization		 0.212013
Approximate Gaussian process inference for the drift function in stochastic differential equations · NIPS 2013
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
gaussian process		 0.212013
Approximate Gaussian process inference for the drift function in stochastic differential equations · NIPS 2013
Machine learning › Probabilistic and Bayesian machine learning › structured models
latent variable model		 0.212013
Approximate Gaussian process inference for the drift function in stochastic differential equations · NIPS 2013
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › gaussian process › sparse gaussian process
sparse gaussian process regression		 0.212013
Approximate Gaussian process inference for the drift function in stochastic differential equations · NIPS 2013

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

sparse gaussian process regression · 0.2piecewise linearization · 0.2gaussian process prior · 0.2
YearPublicationVenuePosition
2013 Approximate Gaussian process inference for the drift function in stochastic differential equations
abstract
We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobserved, latent dynamics between observations. The posterior over states is approximated by a piecewise linearized process and the MAP estimation of the drift is facilitated by a sparse Gaussian process regression.
Andreas Ruttor, Philipp Batz, Manfred Opper
 NIPS2