Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm. - Kristian Kersting; Niels Landwehr (Probabilistic Graphical Models, 2002)

nFOIL: Integrating Naïve Bayes and FOIL. - Niels Landwehr; Kristian Kersting; Luc De Raedt (AAAI, 2005)

Logistic Model Trees. - Niels Landwehr; Mark Hall 002; Eibe Frank (Mach. Learn., 2005)

kFOIL: Learning Simple Relational Kernels. - Niels Landwehr; Andrea Passerini; Luc De Raedt; Paolo Frasconi (AAAI, 2006)

r-grams: Relational Grams. - Niels Landwehr; Luc De Raedt (IJCAI, 2007)

Constrained hidden Markov models for population-based haplotyping. - Niels Landwehr; Taneli Mielikäinen; Lauri Eronen; Hannu Toivonen; Heikki Mannila (BMC Bioinform., 2007)

Combining haplotypers - Matti Kääriäinen; Niels Landwehr; Sampsa Lappalainen; Taneli Mielikäinen (CoRR, 2007)

Integrating Naïve Bayes and FOIL. - Niels Landwehr; Kristian Kersting; Luc De Raedt (J. Mach. Learn. Res., 2007)

Boosting Relational Sequence Alignments. - Andreas Karwath; Kristian Kersting; Niels Landwehr (ICDM, 2008)

Modeling interleaved hidden processes. - Niels Landwehr (ICML, 2008)

Relational Sequence Learning. - Kristian Kersting; Luc De Raedt; Bernd Gutmann; Andreas Karwath; Niels Landwehr (Probabilistic Inductive Logic Programming, 2008)

Probabilistic Logic Learning from Haplotype Data. - Niels Landwehr; Taneli Mielikäinen (Probabilistic Inductive Logic Programming, 2008)

A Simple Model for Sequences of Relational State Descriptions. - Ingo Thon; Niels Landwehr; Luc De Raedt (ECML/PKDD (2), 2008)

Relational Transformation-based Tagging for Activity Recognition. - Niels Landwehr; Bernd Gutmann; Ingo Thon; Luc De Raedt; Matthai Philipose (Fundam. Informaticae, 2008)

An Exercise with Statistical Relational Learning Systems. - Maurice Bruynooghe; Daan Fierens; Bernd Gutmann; Angelika Kimmig; Niels Landwehr; Wannes Meert; Ingo Thon; Luc De Raedt (MLG/SRL@ILP, 2009)

Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract. - Niels Landwehr (SIGKDD Explor., 2009)

Active Risk Estimation. - Christoph Sawade; Niels Landwehr; Steffen Bickel; Tobias Scheffer (ICML, 2010)

Active Estimation of F-Measures. - Christoph Sawade; Niels Landwehr; Tobias Scheffer (NIPS, 2010)

Fast learning of relational kernels. - Niels Landwehr; Andrea Passerini; Luc De Raedt; Paolo Frasconi (Mach. Learn., 2010)

Relational Feature Mining with Hierarchical Multitask kFOIL. - Elisa Cilia; Niels Landwehr; Andrea Passerini (Fundam. Informaticae, 2011)

Stochastic relational processes: Efficient inference and applications. - Ingo Thon; Niels Landwehr; Luc De Raedt (Mach. Learn., 2011)

Learning to Identify Regular Expressions that Describe Email Campaigns. - Paul Prasse; Christoph Sawade; Niels Landwehr; Tobias Scheffer (ICML, 2012)

Active Comparison of Prediction Models. - Christoph Sawade; Niels Landwehr; Tobias Scheffer (NIPS, 2012)

Active Evaluation of Ranking Functions Based on Graded Relevance (Extended Abstract). - Christoph Sawade; Steffen Bickel; Timo von Oertzen; Tobias Scheffer; Niels Landwehr (IJCAI, 2013)

Active evaluation of ranking functions based on graded relevance. - Christoph Sawade; Steffen Bickel; Timo von Oertzen; Tobias Scheffer; Niels Landwehr (Mach. Learn., 2013)

A Model of Individual Differences in Gaze Control During Reading. - Niels Landwehr; Sebastian Arzt; Tobias Scheffer; Reinhold Kliegl (EMNLP, 2014)

Joint Prediction of Topics in a URL Hierarchy. - Michael Großhans; Christoph Sawade; Tobias Scheffer; Niels Landwehr (ECML/PKDD (1), 2014)

Varying-coefficient models with isotropic Gaussian process priors. - Matthias Bussas; Christoph Sawade; Tobias Scheffer; Niels Landwehr (CoRR, 2015)

Learning to identify concise regular expressions that describe email campaigns. - Paul Prasse; Christoph Sawade; Niels Landwehr; Tobias Scheffer (J. Mach. Learn. Res., 2015)

A Semiparametric Model for Bayesian Reader Identification. - Ahmed AbdelWahab; Reinhold Kliegl; Niels Landwehr (EMNLP, 2016)

Varying-coefficient models for geospatial transfer learning. - Matthias Bussas; Christoph Sawade; Nicolas Kühn; Tobias Scheffer; Niels Landwehr (Mach. Learn., 2017)

Detecting Autism by Analyzing a Simulated Social Interaction. - Hanna Drimalla; Niels Landwehr; Irina Baskow; Behnoush Behnia; Stefan Roepke; Isabel Dziobek; Tobias Scheffer (ECML/PKDD (1), 2018)

A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. - Silvia Makowski; Lena A. Jäger; Ahmed AbdelWahab; Niels Landwehr; Tobias Scheffer (ECML/PKDD (1), 2018)

Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics. - Ahmed AbdelWahab; Niels Landwehr (ECML/PKDD (2), 2019)

Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). - Hanna Drimalla; Tobias Scheffer; Niels Landwehr; Irina Baskow; Stefan Roepke; Behnoush Behnia; Isabel Dziobek (npj Digit. Medicine, 2020)

Improved digital image-based assessment of soil aggregate size by applying convolutional neural networks. - Pendar Alirezazadeh; Fatemeh Rahimi-Ajdadi; Yousef Abbaspour-Gilandeh; Niels Landwehr; Hamed Tavakoli (Comput. Electron. Agric., 2021)

Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building. - Khem Raj Gautam; Guoqiang Zhang 007; Niels Landwehr; Julian Adolphs (Comput. Electron. Agric., 2021)

Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. - Hamed Tavakoli; Pendar Alirezazadeh; A. Hedayatipour; A. H. Banijamali Nasib; Niels Landwehr (Comput. Electron. Agric., 2021)

Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops. - Tibor de Camargo; Michael Schirrmann; Niels Landwehr; Karl-Heinz Dammer; Michael Pflanz (Remote. Sens., 2021)

Author Correction: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). - Hanna Drimalla; Tobias Scheffer; Niels Landwehr; Irina Baskow; Stefan Roepke; Behnoush Behnia; Isabel Dziobek (npj Digit. Medicine, 2022)

Deep Distributional Sequence Embeddings Based on a Wasserstein Loss. - Ahmed AbdelWahab; Niels Landwehr (Neural Process. Lett., 2022)

Hyperbolic Geometry in Computer Vision: A Novel Framework for Convolutional Neural Networks. - Ahmad Bdeir; Kristian Schwethelm; Niels Landwehr (CoRR, 2023)

POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations. - Saddam Hijazi; Melina A. Freitag; Niels Landwehr (Adv. Model. Simul. Eng. Sci., 2023)

Fully Hyperbolic Convolutional Neural Networks for Computer Vision. - Ahmad Bdeir; Kristian Schwethelm; Niels Landwehr (ICLR, 2024)

Optimizing Curvature Learning for Robust Hyperbolic Deep Learning in Computer Vision. - Ahmad Bdeir; Niels Landwehr (CoRR, 2024)