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Lars Schmidt-Thieme

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Assoziationsregel-Algorithmen für Daten mit komplexer Struktur: mit Anwendungen im Web Mining
L. Schmidt-Thieme. Uni Karlsruhe, (2003)
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Die formale Gestaltung von Exposition und Reprise in den Streichquartetten Haydns
L. Schmidt-Thieme. Uni Heidelberg, (1998)
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Formal concept analysis and tag recommendations in collaborative tagging systems
R. Jäschke. Uni Kassel, (2019)
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Other Publications
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Towards Distributed Pairwise Ranking using Implicit Feedback.(2018)
M. Jameel, N. Schilling, and L. Schmidt-Thieme. ACM
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization.(2018)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme.
Collaborative SVM classification in scale-free peer-to-peer networks.(2017)
M. Khan, L. Schmidt-Thieme, and A. Nanopoulos.
Channel masking for multivariate time series shapelets.(2017)
D. Raychaudhuri, J. Grabocka, and L. Schmidt-Thieme.
Finding Hierarchy of Topics from Twitter Data.(2017)
N. Duong-Trung, N. Schilling, and L. Schmidt-Thieme. CEUR-WS.org
Personalized Tag Recommendation for Images Using Deep Transfer Learning.(2017)
H. Nguyen, M. Wistuba, and L. Schmidt-Thieme. Springer
On Discovering the Number of Document Topics via Conceptual Latent Space.(2017)
N. Duong-Trung, and L. Schmidt-Thieme. ACM
Personalized Deep Learning for Tag Recommendation.(2017)
H. Nguyen, M. Wistuba, J. Grabocka, L. Drumond, and L. Schmidt-Thieme.
Bank Card Usage Prediction Exploiting Geolocation Information.(2016)
M. Wistuba, N. Duong-Trung, N. Schilling, and L. Schmidt-Thieme.
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts.(2016)
N. Schilling, M. Wistuba, and L. Schmidt-Thieme. Springer
Latent Time-Series Motifs(2016)
J. Grabocka, N. Schilling, and L. Schmidt-Thieme. ACM
Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems.(2016)
R. Janning, C. Schatten, and L. Schmidt-Thieme.
Fast classification of univariate and multivariate time series through shapelet discovery.(2016)
J. Grabocka, M. Wistuba, and L. Schmidt-Thieme.
Learning DTW-Shapelets for Time-Series Classification.(2016)
M. Shah, J. Grabocka, N. Schilling, M. Wistuba, and L. Schmidt-Thieme. ACM
Multi-Relational Learning at Scale with ADMM.(2016)
L. Drumond, E. Diaz-Aviles, and L. Schmidt-Thieme.
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization.(2016)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. Springer
Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream.(2016)
N. Duong-Trung, N. Schilling, L. Drumond, and L. Schmidt-Thieme. CEUR-WS.org
Hyperparameter Optimization Machines.(2016)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. IEEE
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF.(2016)
R. Aga, L. Drumond, C. Wartena, and L. Schmidt-Thieme. ACL
Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression.(2016)
N. Duong-Trung, N. Schilling, and L. Schmidt-Thieme. ACM
Learning Thesaurus Relations from Distributional Features.(2016)
R. Aga, C. Wartena, L. Drumond, and L. Schmidt-Thieme. European Language Resources Association (ELRA)
Student Progress Modeling with Skills Deficiency Aware Kalman Filters.(2016)
C. Schatten, and L. Schmidt-Thieme. SciTePress
Optimal Time-Series Motifs.(2015)
J. Grabocka, N. Schilling, and L. Schmidt-Thieme.
A supervised active learning framework for recommender systems based on decision trees.(2015)
R. Karimi, A. Nanopoulos, and L. Schmidt-Thieme.
A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets.(2015)
L. Voß, C. Schatten, C. Mazziotti, and L. Schmidt-Thieme. International Educational Data Mining Society (IEDMS)
Improving Automatic Affect Recognition on Low-Level Speech Features in Intelligent Tutoring Systems.(2015)
R. Janning, C. Schatten, and L. Schmidt-Thieme. Springer
Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS.(2015)
C. Schatten, R. Janning, and L. Schmidt-Thieme. AAAI Press
How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems.(2015)
R. Janning, C. Schatten, and L. Schmidt-Thieme. International Educational Data Mining Society (IEDMS)
Geo_ML @ MediaEval Placing Task 2015.(2015)
N. Duong-Trung, M. Wistuba, L. Drumond, and L. Schmidt-Thieme. CEUR-WS.org
Learning Data Set Similarities for Hyperparameter Optimization Initializations.(2015)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. CEUR-WS.org
Beyond Manual Tuning of Hyperparameters.(2015)
F. Hutter, J. Lücke, and L. Schmidt-Thieme.
Scalable Discovery of Time-Series Shapelets.(2015)
J. Grabocka, M. Wistuba, and L. Schmidt-Thieme.
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization.(2015)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. Springer
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons.(2015)
N. Schilling, M. Wistuba, L. Drumond, and L. Schmidt-Thieme. IEEE Computer Society
Comparing Prediction Models for Active Learning in Recommender Systems.(2015)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. CEUR-WS.org
Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials.(2015)
J. Grabocka, M. Wistuba, and L. Schmidt-Thieme.
Ultra-Fast Shapelets for Time Series Classification.(2015)
M. Wistuba, J. Grabocka, and L. Schmidt-Thieme.
Learning hyperparameter optimization initializations.(2015)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. IEEE
Sequential Model-Free Hyperparameter Tuning.(2015)
M. Wistuba, N. Schilling, and L. Schmidt-Thieme. IEEE Computer Society
Recognizing Perceived Task Difficulty from Speech and Pause Histograms.(2015)
R. Janning, C. Schatten, and L. Schmidt-Thieme. CEUR-WS.org
Recognizing Perceived Task Difficulty from Speech, Pause Histograms.(2015)
R. Janning, C. Schatten, and L. Schmidt-Thieme. CEUR-WS.org
Hyperparameter Optimization with Factorized Multilayer Perceptrons.(2015)
N. Schilling, M. Wistuba, L. Drumond, and L. Schmidt-Thieme. Springer
Multi-relational Factorization Models for Student Modeling in Intelligent Tutoring Systems.(2015)
N. Thai-Nghe, and L. Schmidt-Thieme. IEEE
Realistic optimal policies for energy-efficient train driving.(2014)
J. Grabocka, A. Dalkalitsis, A. Lois, E. Katsaros, and L. Schmidt-Thieme. IEEE
Buried pipe localization using an iterative geometric clustering on GPR data.(2014)
R. Janning, A. Busche, T. Horváth, and L. Schmidt-Thieme.
Learning time-series shapelets.(2014)
J. Grabocka, N. Schilling, M. Wistuba, and L. Schmidt-Thieme. ACM
Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems.(2014)
C. Schatten, R. Janning, M. Mavrikis, and L. Schmidt-Thieme. International Educational Data Mining Society (IEDMS)
Supervised Nonlinear Factorizations Excel In Semi-supervised Regression.(2014)
J. Grabocka, E. Bedalli, and L. Schmidt-Thieme. Springer
Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems.(2014)
C. Schatten, M. Wistuba, L. Schmidt-Thieme, and S. Santos. IEEE Computer Society
Invariant time-series factorization.(2014)
J. Grabocka, and L. Schmidt-Thieme.
Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach.(2014)
C. Schatten, R. Janning, and L. Schmidt-Thieme. Springer
Local Feature Extractors Accelerating HNNP for Phoneme Recognition.(2014)
R. Janning, C. Schatten, and L. Schmidt-Thieme. Springer
Analyzing cooperative lane change models for connected vehicles.(2014)
M. Khan, P. Basaras, L. Schmidt-Thieme, A. Nanopoulos, and D. Katsaros. IEEE
Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems.(2014)
R. Janning, C. Schatten, and L. Schmidt-Thieme. Springer
Improved Questionnaire Trees for Active Learning in Recommender Systems.(2014)
R. Karimi, A. Nanopoulos, and L. Schmidt-Thieme. CEUR-WS.org
Adaptive Content Sequencing without Domain Information.(2014)
C. Schatten, and L. Schmidt-Thieme. SciTePress
An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems.(2014)
R. Janning, C. Schatten, L. Schmidt-Thieme, G. Backfried, and N. Pfannerer. IEEE Computer Society
Optimizing Multi-Relational Factorization Models for Multiple Target Relations(2014)
L. Drumond, E. Diaz-Aviles, L. Schmidt-Thieme, and W. Nejdl. ACM
Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems.(2014)
R. Janning, C. Schatten, and L. Schmidt-Thieme. CEUR-WS.org
Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification.(2014)
L. Drumond, L. Schmidt-Thieme, C. Freudenthaler, and A. Krohn-Grimberghe. Springer
Automatic Subclasses Estimation for a Better Classification with HNNP.(2014)
R. Janning, C. Schatten, and L. Schmidt-Thieme. Springer
P2P RVM for Distributed Classification.(2013)
M. Khan, A. Nanopoulos, and L. Schmidt-Thieme. Springer
An Evaluation Measure for Learning from Imbalanced Data Based on Asymmetric Beta Distribution.(2013)
N. Thai-Nghe, Z. Gantner, and L. Schmidt-Thieme. Springer
Event Prediction in Pharyngeal High-Resolution Manometry.(2013)
N. Schilling, A. Busche, S. Miller, M. Jungheim, M. Ptok, and L. Schmidt-Thieme. Springer
Hough Transform and Kirchhoff Migration for Supervised GPR Data Analysis.(2013)
A. Busche, D. Seyfried, and L. Schmidt-Thieme. Springer
HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information.(2013)
R. Janning, C. Schatten, and L. Schmidt-Thieme. IEEE Computer Society
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems.(2013)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. Universitätsbibliothek Bamberg
Supervised Dimensionality Reduction via Nonlinear Target Estimation.(2013)
J. Grabocka, L. Drumond, and L. Schmidt-Thieme. Springer
Towards real-time collaborative filtering for big fast data(2013)
E. Diaz-Aviles, W. Nejdl, L. Drumond, and L. Schmidt-Thieme. International World Wide Web Conferences Steering Committee
Time-Series Classification Through Histograms of Symbolic Polynomials.(2013)
J. Grabocka, M. Wistuba, and L. Schmidt-Thieme.
Invariant Factorization Of Time-Series.(2013)
J. Grabocka, and L. Schmidt-Thieme.
Supervised Clustering of Social Media Streams.(2013)
M. Wistuba, and L. Schmidt-Thieme. CEUR-WS.org
Move Prediction in Go - Modelling Feature Interactions Using Latent Factors.(2013)
M. Wistuba, and L. Schmidt-Thieme. Springer
Analysing the Potential Impact of Labeling Disagreements for Engineering Sensor Data.(2013)
A. Busche, R. Janning, and L. Schmidt-Thieme. Universitätsbibliothek Bamberg
Factorized Decision Trees for Active Learning in Recommender Systems.(2013)
R. Karimi, M. Wistuba, A. Nanopoulos, and L. Schmidt-Thieme. IEEE Computer Society
Recommender Systems for Social Tagging Systems(2012)
L. Balby Marinho, A. Hotho, R. Jäschke, A. Nanopoulos, S. Rendle, L. Schmidt-Thieme, G. Stumme, and P. Symeonidis. Springer
Invariant Time-Series Classification.(2012)
J. Grabocka, A. Nanopoulos, and L. Schmidt-Thieme. Springer
GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing.(2012)
R. Janning, T. Horváth, A. Busche, and L. Schmidt-Thieme. Springer
Factorization techniques for student performance classification and ranking.(2012)
L. Drumond, N. Thai-Nghe, T. Horváth, and L. Schmidt-Thieme. CEUR-WS.org
A Unifying Framework for GPR Image Reconstruction.(2012)
A. Busche, R. Janning, T. Horváth, and L. Schmidt-Thieme. Springer
BPR: Bayesian Personalized Ranking from Implicit Feedback(2012)
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme.
Exploiting the characteristics of matrix factorization for active learning in recommender systems.(2012)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. ACM
Multinomial SVM Item Recommender for Repeat-Buying Scenarios.(2012)
C. Lichtenthäler, and L. Schmidt-Thieme. Springer
Using factorization machines for student modeling.(2012)
N. Thai-Nghe, L. Drumond, T. Horváth, and L. Schmidt-Thieme. CEUR-WS.org
Multi-relational matrix factorization using bayesian personalized ranking for social network data.(2012)
A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. ACM
Personalized Ranking for Non-Uniformly Sampled Items.(2012)
Z. Gantner, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. JMLR.org
GRAMOFON: General model-selection framework based on networks.(2012)
K. Buza, A. Nanopoulos, T. Horváth, and L. Schmidt-Thieme.
Classification of Sparse Time Series via Supervised Matrix Factorization.(2012)
J. Grabocka, A. Nanopoulos, and L. Schmidt-Thieme. AAAI Press
Real-time Top-N Recommendation in Social Streams(2012)
E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. ACM
Predicting RDF triples in incomplete knowledge bases with tensor factorization.(2012)
L. Drumond, S. Rendle, and L. Schmidt-Thieme. ACM
MyMediaLite: a free recommender system library(2011)
Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. ACM
Fast Classification of Electrocardiograph Signals via Instance Selection.(2011)
K. Buza, A. Nanopoulos, L. Schmidt-Thieme, and J. Koller. IEEE
A new evaluation measure for learning from imbalanced data.(2011)
N. Thai-Nghe, Z. Gantner, and L. Schmidt-Thieme. IEEE
Active Learning for Technology Enhanced Learning(2011)
A. Krohn-Grimberghe, A. Busche, A. Nanopoulos, and L. Schmidt-Thieme.
Active learning for aspect model in recommender systems.(2011)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. IEEE
RFID-Enhanced Museum for Interactive Experience.(2011)
R. Karimi, A. Nanopoulos, and L. Schmidt-Thieme. Springer
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems.(2011)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. IEEE Computer Society
Factorization Models for Forecasting Student Performance.(2011)
N. Thai-Nghe, T. Horváth, and L. Schmidt-Thieme. www.educationaldatamining.org
Non-myopic active learning for recommender systems based on Matrix Factorization.(2011)
R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. IEEE Systems, Man, and Cybernetics Society
Personalized Forecasting Student Performance.(2011)
N. Thai-Nghe, T. Horváth, and L. Schmidt-Thieme. IEEE Computer Society
Social Tagging Recommender Systems.(2011)
L. Marinho, A. Nanopoulos, L. Schmidt-Thieme, R. Jäschke, A. Hotho, G. Stumme, and P. Symeonidis. Springer
IQ estimation for accurate time-series classification.(2011)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. IEEE
INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification.(2011)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. Springer
Fusion of Similarity Measures for Time Series Classification.(2011)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. Springer
Matrix and Tensor Factorization for Predicting Student Performance.(2011)
N. Thai-Nghe, L. Drumond, T. Horváth, A. Nanopoulos, and L. Schmidt-Thieme. SciTePress
Recommender system for predicting student performance.(2010)
N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, and L. Schmidt-Thieme.
Individualized Error Estimation for Classification and Regression Models.(2010)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. Springer
Cost-sensitive learning methods for imbalanced data.(2010)
N. Thai-Nghe, Z. Gantner, and L. Schmidt-Thieme. IEEE
Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation(2010)
S. Rendle, and L. Schmidt-Thieme. ACM
Factorizing personalized Markov chains for next-basket recommendation(2010)
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme.
Learning attribute-to-feature mappings for cold-start recommendations(2010)
Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme.
Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems(2010)
C. Preisach, L. Marinho, and L. Schmidt-Thieme. Springer
Integrating OLAP and recommender systems: an evaluation perspective.(2010)
A. Krohn-Grimberghe, A. Nanopoulos, and L. Schmidt-Thieme. ACM
Mining Music Playlogs for Next Song Recommendations.(2010)
A. Busche, A. Krohn-Grimberghe, and L. Schmidt-Thieme.
Time-Series Classification Based on Individualised Error Prediction.(2010)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. IEEE Computer Society
Workshop on user-centric evaluation of recommender systems and their interfaces.(2010)
B. Knijnenburg, L. Schmidt-Thieme, and D. Bollen. ACM
Graph-Based Model-Selection Framework for Large Ensembles.(2010)
K. Buza, A. Nanopoulos, and L. Schmidt-Thieme. Springer
A Novel Multidimensional Framework for Evaluating Recommender Systems.(2010)
A. Krohn-Grimberghe, A. Nanopoulos, and L. Schmidt-Thieme.
Optimal Ranking for Video Recommendation.(2009)
Z. Gantner, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Springer
BPR: Bayesian Personalized Ranking from Implicit Feedback(2009)
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme.
Cross-Tagging for Personalized Open Social Networking(2009)
A. Stewart, E. Diaz-Aviles, L. Marinho, A. Nanopoulos, L. Schmidt-Thieme, and W. Nejdl. ACM
Relational Classification for Personalized Tag Recommendation(2009)
L. Marinho, C. Preisach, and L. Schmidt-Thieme. ACM
Automatic Content-Based Categorization of Wikipedia Articles.(2009)
Z. Gantner, and L. Schmidt-Thieme. Association for Computational Linguistics
Learning optimal ranking with tensor factorization for tag recommendation(2009)
S. Rendle, L. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. ACM
Improving Academic Performance Prediction by Dealing with Class Imbalance.(2009)
N. Thai-Nghe, A. Busche, and L. Schmidt-Thieme. IEEE Computer Society
Factor Models for Tag Recommendation in BibSonomy(2009)
S. Rendle, and L. Schmidt-Thieme. ACM
Swarming to rank for information retrieval.(2009)
E. Diaz-Aviles, W. Nejdl, and L. Schmidt-Thieme. ACM
Learning to Extract Relations for Relational Classification.(2009)
S. Rendle, C. Preisach, and L. Schmidt-Thieme. Springer
Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference.(2008)
S. Rendle, and L. Schmidt-Thieme. IEEE Computer Society
Tag-aware recommender systems by fusion of collaborative filtering algorithms(2008)
K. Tso-Sutter, L. Marinho, and L. Schmidt-Thieme. ACM
Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems(2008)
E. Diaz-Aviles, L. Schmidt-Thieme, and C. Ziegler.
Folksonomy-Based Collabulary Learning(2008)
L. Marinho, K. Buza, and L. Schmidt-Thieme. Springer
Tag Recommendations in Social Bookmarking Systems(2008)
R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. IOS Press
Ensembles of relational classifiers.(2008)
C. Preisach, and L. Schmidt-Thieme.
Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems(2008)
S. Rendle, and L. Schmidt-Thieme. ACM
Scaling Record Linkage to Non-uniform Distributed Class Sizes.(2008)
S. Rendle, and L. Schmidt-Thieme. Springer
Motif-Based Classification of Time Series with Bayesian Networks and SVMs.(2008)
K. Buza, and L. Schmidt-Thieme. Springer
Information Integration of Partially Labeled Data.(2007)
S. Rendle, and L. Schmidt-Thieme. Springer
Tag Recommendations in Folksonomies(2007)
R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Springer
Guest Editors' Introduction: Recommender Systems.(2007)
A. Felfernig, G. Friedrich, and L. Schmidt-Thieme.
Comparison of Recommender System Algorithms focusing on the New-Item and User-Bias Problem(2007)
S. Hauger, K. Tso, and L. Schmidt-Thieme.
Collaborative Tag Recommendations.(2007)
L. Marinho, and L. Schmidt-Thieme. Springer
Supporting Collaborative Hierarchical Classification: Bookmarks as an Example(2007)
D. Benz, K. Tso, and L. Schmidt-Thieme.
Object Identification with Constraints(2006)
S. Rendle, and L. Schmidt-Thieme. IEEE Computer Society
Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data.(2006)
K. Tso, and L. Schmidt-Thieme. Springer
Evaluation of attribute-aware recommender system algorithms on data with varying characteristics(2006)
K. Tso, and L. Schmidt-Thieme. Springer
Ideas and Improvements for Semantic Wikis.(2006)
J. Fischer, Z. Gantner, S. Rendle, M. Stritt, and L. Schmidt-Thieme.
A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative.(2006)
R. Backofen, H. Borrmann, W. Deck, A. Dedner, L. Raedt, K. Desch, M. Diesmann, M. Geier, A. Greiner, W. Hess, J. Honerkamp, S. Jankowski, I. Krossing, A. Liehr, A. Karwath, R. Klöfkorn, R. Pesché, T. Potjans, M. Röttger, L. Schmidt-Thieme, G. Schneider, B. Voß, B. Wiebelt, P. Wienemann, and V. Winterer.
Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data.(2006)
K. Tso, and L. Schmidt-Thieme.
Relational Ensemble Classification.(2006)
C. Preisach, and L. Schmidt-Thieme.
Attribute Aware Anonymous Recommender Systems.(2006)
M. Stritt, K. Tso, and L. Schmidt-Thieme. Springer
Performance Drivers for Depth-First Frequent Pattern Mining.(2005)
L. Schmidt-Thieme, and M. Schader. Springer
Ontology-Based Query Refinement for Semantic Portals.(2005)
J. Hartmann, N. Stojanovic, R. Studer, and L. Schmidt-Thieme. Springer
The Relation of Closed Itemset Mining, Complete Pruning Strategies and Item Ordering in Apriori-Based FIM Algorithms.(2005)
F. Bodon, and L. Schmidt-Thieme. Springer
Compound Classification Models for Recommender Systems.(2005)
L. Schmidt-Thieme. IEEE Computer Society
Collaborative and Usage-driven Evolution of Personal Ontologies.(2005)
P. Haase, A. Hotho, L. Schmidt-Thieme, and Y. Sure. DFKI
Attribute-aware Collaborative Filtering(2005)
K. Tso, and L. Schmidt-Thieme.
Algorithmic Features of Eclat.(2004)
L. Schmidt-Thieme. CEUR-WS.org
Reservation Price Estimation by Adaptive Conjoint Analysis.(2004)
C. Breidert, M. Hahsler, and L. Schmidt-Thieme. Springer
Taxonomy-driven computation of product recommendations.(2004)
C. Ziegler, G. Lausen, and L. Schmidt-Thieme. ACM
Java - eine Einführung (4. Aufl.).(2003)
M. Schader, and L. Schmidt-Thieme. Springer
Mining Generalized Association Rules for Sequential and Path Data.(2001)
W. Gaul, and L. Schmidt-Thieme. IEEE Computer Society

Other Publications of same named Authors
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Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks.(2020)
M. ul Islam Arif, M. Jameel, J. Grabocka, and L. Schmidt-Thieme. CEUR-WS.org
Gait Verification using Deep Learning with a Pairwise Loss.(2019)
V. Yalavarthi, J. Grabocka, H. Mandalapu, and L. Schmidt-Thieme. Gesellschaft für Informatik e.V.
MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems.(2020)
A. Rashed, S. Jawed, L. Schmidt-Thieme, and A. Hintsches. ACM
An efficient evolutionary solution to the joint order batching - order picking planning problem.(2020)
R. Lucato, J. Falkner, and L. Schmidt-Thieme. ACM
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention.(2020)
J. Falkner, and L. Schmidt-Thieme.
HIDRA: Head Initialization across Dynamic targets for Robust Architectures.(2020)
R. Drumond, L. Brinkmeyer, J. Grabocka, and L. Schmidt-Thieme. SIAM
Learning Through Non-linearly Supervised Dimensionality Reduction.(2015)
J. Grabocka, and L. Schmidt-Thieme. Springer
A Deep Multi-task Approach for Residual Value Forecasting.(2019)
A. Rashed, S. Jawed, J. Rehberg, J. Grabocka, L. Schmidt-Thieme, and A. Hintsches. Springer
Multi-step Forecasting via Multi-task Learning.(2019)
S. Jawed, A. Rashed, and L. Schmidt-Thieme. IEEE
Directly Optimizing IoU for Bounding Box Localization.(2019)
M. ul Islam Arif, M. Jameel, and L. Schmidt-Thieme. Springer
Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights.(2019)
A. Rashed, J. Grabocka, and L. Schmidt-Thieme. Springer
HIDRA: Head Initialization across Dynamic targets for Robust Architectures.(2019)
R. Drumond, L. Brinkmeyer, J. Grabocka, and L. Schmidt-Thieme.
A Hybrid Convolutional Approach for Parking Availability Prediction.(2019)
H. Jomaa, J. Grabocka, L. Schmidt-Thieme, and A. Borek. IEEE
Chameleon: Learning Model Initializations Across Tasks With Different Schemas.(2019)
L. Brinkmeyer, R. Drumond, R. Scholz, J. Grabocka, and L. Schmidt-Thieme.
Handwritten Amharic Character Recognition Using a Convolutional Neural Network.(2019)
M. Gondere, L. Schmidt-Thieme, A. Boltena, and H. Jomaa.
Attribute-aware non-linear co-embeddings of graph features.(2019)
A. Rashed, J. Grabocka, and L. Schmidt-Thieme. ACM
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings.(2019)
A. Rashed, J. Grabocka, and L. Schmidt-Thieme. ACM
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning.(2019)
H. Jomaa, J. Grabocka, and L. Schmidt-Thieme.
In Hindsight: A Smooth Reward for Steady Exploration.(2019)
H. Jomaa, J. Grabocka, and L. Schmidt-Thieme.
Multi-Label Network Classification via Weighted Personalized Factorizations.(2019)
A. Rashed, J. Grabocka, and L. Schmidt-Thieme. SciTePress
Dataset2Vec: Learning Dataset Meta-Features.(2019)
H. Jomaa, J. Grabocka, and L. Schmidt-Thieme.