Mining Generalized Association Rules for Sequential and Path Data. - Wolfgang Gaul; Lars Schmidt-Thieme (ICDM, 2001)

Java - eine Einführung (4. Aufl.). - Martin Schader; Lars Schmidt-Thieme (Springer, 2003)

Assoziationsregel-Algorithmen für Daten mit komplexer Struktur: mit Anwendungen im Web Mining. - Lars Schmidt-Thieme (2003)

Taxonomy-driven computation of product recommendations. - Cai-Nicolas Ziegler; Georg Lausen; Lars Schmidt-Thieme (CIKM, 2004)

Algorithmic Features of Eclat. - Lars Schmidt-Thieme (FIMI, 2004)

Reservation Price Estimation by Adaptive Conjoint Analysis. - Christoph Breidert; Michael Hahsler; Lars Schmidt-Thieme (GfKl, 2004)

Ontology-Based Query Refinement for Semantic Portals. - Jens Hartmann 001; Nenad Stojanovic; Rudi Studer; Lars Schmidt-Thieme (From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments, 2005)

Collaborative and Usage-Driven Evolution of Personal Ontologies. - Peter Haase 001; Andreas Hotho; Lars Schmidt-Thieme; York Sure (ESWC, 2005)

Attribute-aware Collaborative Filtering. - Karen H. L. Tso; Lars Schmidt-Thieme (GfKl, 2005)

Fachinformationssystem Informatik (FIS-I) und Semantische Technologien für Informationsportale (SemIPort). - Peter Fankhauser; Norbert Fuhr; Jens Hartmann 001; Anthony Jameson; Claus-Peter Klas; Stefan Klink; Agnes Koschmider; Sascha Kriewel; Patrick Lehti; Peter Luksch 002; Ernst W. Mayr; Andreas Oberweis; et al. (GI Jahrestagung (2), 2005)

Compound Classification Models for Recommender Systems. - Lars Schmidt-Thieme (ICDM, 2005)

The Relation of Closed Itemset Mining, Complete Pruning Strategies and Item Ordering in Apriori-Based FIM Algorithms. - Ferenc Bodon; Lars Schmidt-Thieme (PKDD, 2005)

Performance Drivers for Depth-First Frequent Pattern Mining. - Lars Schmidt-Thieme; Martin Schader (Data Analysis and Decision Support, 2005)

Ideas and Improvements for Semantic Wikis. - Jochen Fischer; Zeno Gantner; Steffen Rendle; Manuel Stritt; Lars Schmidt-Thieme (ESWC, 2006)

Attribute Aware Anonymous Recommender Systems. - Manuel Stritt; Karen H. L. Tso; Lars Schmidt-Thieme (GfKl, 2006)

Relational Ensemble Classification. - Christine Preisach; Lars Schmidt-Thieme (ICDM, 2006)

Object Identification with Constraints. - Steffen Rendle; Lars Schmidt-Thieme (ICDM, 2006)

Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics. - Karen H. L. Tso; Lars Schmidt-Thieme (PAKDD, 2006)

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data. - Karen H. L. Tso; Lars Schmidt-Thieme (J. Comput., 2006)

A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. - Rolf Backofen; Hans-Gunther Borrmann; Werner Deck; Andreas Dedner; Luc De Raedt; Klaus Desch; Markus Diesmann; Martin Geier; Andreas Greiner; Wolfgang R. Hess; Josef Honerkamp; Stefan Jankowski; et al. (Prax. Inf.verarb. Kommun., 2006)

Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data. - Karen H. L. Tso; Lars Schmidt-Thieme (Data Science and Classification, 2006)

Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem. - Stefan Hauger; Karen H. L. Tso; Lars Schmidt-Thieme (GfKl, 2007)

Collaborative Tag Recommendations. - Leandro Balby Marinho; Lars Schmidt-Thieme (GfKl, 2007)

Information Integration of Partially Labeled Data. - Steffen Rendle; Lars Schmidt-Thieme (GfKl, 2007)

Combining multi-distributed mixture models and bayesian networks for semi-supervised learning. - Manuel Stritt; Lars Schmidt-Thieme; Gerhard Poeppel (ICMLA, 2007)

Tag Recommendations in Folksonomies. - Robert Jäschke; Leandro Balby Marinho; Andreas Hotho; Lars Schmidt-Thieme; Gerd Stumme (PKDD, 2007)

Supporting collaborative hierarchical classification: Bookmarks as an example. - Dominik Benz; Karen H. L. Tso; Lars Schmidt-Thieme (Comput. Networks, 2007)

Guest Editors' Introduction: Recommender Systems. - Alexander Felfernig; Gerhard Friedrich; Lars Schmidt-Thieme (IEEE Intell. Syst., 2007)

Motif-Based Classification of Time Series with Bayesian Networks and SVMs. - Krisztián Búza; Lars Schmidt-Thieme (GfKl, 2008)

Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference. - Steffen Rendle; Lars Schmidt-Thieme (ICDM, 2008)

Scaling Record Linkage to Non-uniform Distributed Class Sizes. - Steffen Rendle; Lars Schmidt-Thieme (PAKDD, 2008)

Online-updating regularized kernel matrix factorization models for large-scale recommender systems. - Steffen Rendle; Lars Schmidt-Thieme (RecSys, 2008)

Tag-aware recommender systems by fusion of collaborative filtering algorithms. - Karen H. L. Tso-Sutter; Leandro Balby Marinho; Lars Schmidt-Thieme (SAC, 2008)

Folksonomy-Based Collabulary Learning. - Leandro Balby Marinho; Krisztián Búza; Lars Schmidt-Thieme (ISWC, 2008)

Tag recommendations in social bookmarking systems. - Robert Jäschke; Leandro Balby Marinho; Andreas Hotho; Lars Schmidt-Thieme; Gerd Stumme (AI Commun., 2008)

Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems - Ernesto Diaz-Aviles; Lars Schmidt-Thieme; Cai-Nicolas Ziegler (CoRR, 2008)

Ensembles of relational classifiers. - Christine Preisach; Lars Schmidt-Thieme (Knowl. Inf. Syst., 2008)

Automatic Content-Based Categorization of Wikipedia Articles. - Zeno Gantner; Lars Schmidt-Thieme (PWNLP@IJCNLP, 2009)

Swarming to rank for information retrieval. - Ernesto Diaz-Aviles; Wolfgang Nejdl; Lars Schmidt-Thieme (GECCO, 2009)

Cross-tagging for personalized open social networking. - Avare Stewart; Ernesto Diaz-Aviles; Wolfgang Nejdl; Leandro Balby Marinho; Alexandros Nanopoulos; Lars Schmidt-Thieme (Hypertext, 2009)

Improving Academic Performance Prediction by Dealing with Class Imbalance. - Nguyen Thai-Nghe; André Busche; Lars Schmidt-Thieme (ISDA, 2009)

Learning optimal ranking with tensor factorization for tag recommendation. - Steffen Rendle; Leandro Balby Marinho; Alexandros Nanopoulos; Lars Schmidt-Thieme (KDD, 2009)

Learning to Extract Relations for Relational Classification. - Steffen Rendle; Christine Preisach; Lars Schmidt-Thieme (PAKDD, 2009)

Relational Classification for Personalized Tag Recommendation. - Leandro Balby Marinho; Christine Preisach; Lars Schmidt-Thieme (DC@PKDD/ECML, 2009)

Factor Models for Tag Recommendation in BibSonomy. - Steffen Rendle; Lars Schmidt-Thieme (DC@PKDD/ECML, 2009)

BPR: Bayesian Personalized Ranking from Implicit Feedback. - Steffen Rendle; Christoph Freudenthaler; Zeno Gantner; Lars Schmidt-Thieme (UAI, 2009)

Optimal Ranking for Video Recommendation. - Zeno Gantner; Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme (UCMedia, 2009)

Time-Series Classification Based on Individualised Error Prediction. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (CSE, 2010)

Integrating OLAP and recommender systems: an evaluation perspective. - Artus Krohn-Grimberghe; Alexandros Nanopoulos; Lars Schmidt-Thieme (DOLAP, 2010)

Individualized Error Estimation for Classification and Regression Models. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (GfKl, 2010)

Graph-Based Model-Selection Framework for Large Ensembles. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (HAIS (1), 2010)

Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. - Zeno Gantner; Lucas Drumond; Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme (ICDM, 2010)

Cost-sensitive learning methods for imbalanced data. - Nguyen Thai-Nghe; Zeno Gantner; Lars Schmidt-Thieme (IJCNN, 2010)

Mining Music Playlogs for Next Song Recommendations. - André Busche; Artus Krohn-Grimberghe; Lars Schmidt-Thieme (LWA, 2010)

A Novel Multidimensional Framework for Evaluating Recommender Systems. - Artus Krohn-Grimberghe; Alexandros Nanopoulos; Lars Schmidt-Thieme (LWA, 2010)

Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems. - Christine Preisach; Leandro Balby Marinho; Lars Schmidt-Thieme (PAKDD (1), 2010)

Workshop on user-centric evaluation of recommender systems and their interfaces. - Bart P. Knijnenburg; Lars Schmidt-Thieme; Dirk G. F. M. Bollen (RecSys, 2010)

Pairwise interaction tensor factorization for personalized tag recommendation. - Steffen Rendle; Lars Schmidt-Thieme (WSDM, 2010)

Factorizing personalized Markov chains for next-basket recommendation. - Steffen Rendle; Christoph Freudenthaler; Lars Schmidt-Thieme (WWW, 2010)

Recommender system for predicting student performance. - Nguyen Thai-Nghe; Lucas Drumond; Artus Krohn-Grimberghe; Lars Schmidt-Thieme (RecSysTEL@RecSys, 2010)

IQ estimation for accurate time-series classification. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (CIDM, 2011)

Active learning for aspect model in recommender systems. - Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme (CIDM, 2011)

Matrix and Tensor Factorization for Predicting Student Performance. - Nguyen Thai-Nghe; Lucas Drumond; Tomás Horváth; Alexandros Nanopoulos; Lars Schmidt-Thieme (CSEDU (1), 2011)

Active Learning for Technology Enhanced Learning. - Artus Krohn-Grimberghe; André Busche; Alexandros Nanopoulos; Lars Schmidt-Thieme (EC-TEL, 2011)

Factorization Models for Forecasting Student Performance. - Nguyen Thai-Nghe; Tomás Horváth; Lars Schmidt-Thieme (EDM, 2011)

Fusion of Similarity Measures for Time Series Classification. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (HAIS (2), 2011)

Fast Classification of Electrocardiograph Signals via Instance Selection. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme; Júlia Koller (HISB, 2011)

Personalized Forecasting Student Performance. - Nguyen Thai-Nghe; Tomás Horváth; Lars Schmidt-Thieme (ICALT, 2011)

Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. - Timo Reuter; Philipp Cimiano; Lucas Drumond; Krisztián Búza; Lars Schmidt-Thieme (ICWSM, 2011)

Keyword-Based TV Program Recommendation. - Christian Wartena; Wout Slakhorst; Martin Wibbels; Zeno Gantner; Christoph Freudenthaler; Chris Newell; Lars Schmidt-Thieme (ITWP@IJCAI, 2011)

A new evaluation measure for learning from imbalanced data. - Nguyen Thai-Nghe; Zeno Gantner; Lars Schmidt-Thieme (IJCNN, 2011)

Non-myopic active learning for recommender systems based on Matrix Factorization. - Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme (IRI, 2011)

RFID-Enhanced Museum for Interactive Experience. - Rasoul Karimi; Alexandros Nanopoulos; Lars Schmidt-Thieme (MM4CH, 2011)

INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. - Krisztián Búza; Alexandros Nanopoulos; Lars Schmidt-Thieme (PAKDD (2), 2011)

MyMediaLite: a free recommender system library. - Zeno Gantner; Steffen Rendle; Christoph Freudenthaler; Lars Schmidt-Thieme (RecSys, 2011)

Fast context-aware recommendations with factorization machines. - Steffen Rendle; Zeno Gantner; Christoph Freudenthaler; Lars Schmidt-Thieme (SIGIR, 2011)

Social Tagging Recommender Systems. - Leandro Balby Marinho; Alexandros Nanopoulos; Lars Schmidt-Thieme; Robert Jäschke; Andreas Hotho; Gerd Stumme; Panagiotis Symeonidis (Recommender Systems Handbook, 2011)

Recommender Systems for Social Tagging Systems. - Leandro Balby Marinho; Andreas Hotho; Robert Jäschke; Alexandros Nanopoulos; Steffen Rendle; Lars Schmidt-Thieme; Gerd Stumme; Panagiotis Symeonidis (Springer, 2012)

Classification of Sparse Time Series via Supervised Matrix Factorization. - Josif Grabocka; Alexandros Nanopoulos; Lars Schmidt-Thieme (AAAI, 2012)

What is happening right now ... that interests me?: online topic discovery and recommendation in twitter. - Ernesto Diaz-Aviles; Lucas Drumond; Zeno Gantner; Lars Schmidt-Thieme; Wolfgang Nejdl (CIKM, 2012)

A Unifying Framework for GPR Image Reconstruction. - André Busche; Ruth Janning; Tomás Horváth; Lars Schmidt-Thieme (GfKl, 2012)

Multinomial SVM Item Recommender for Repeat-Buying Scenarios. - Christina Lichtenthäler; Lars Schmidt-Thieme (GfKl, 2012)

Efficient Classification of Long Time-Series. - Josif Grabocka; Erind Bedalli; Lars Schmidt-Thieme (ICT Innovations, 2012)

GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing. - Ruth Janning; Tomás Horváth; André Busche; Lars Schmidt-Thieme (AIAI (1), 2012)

Invariant Time-Series Classification. - Josif Grabocka; Alexandros Nanopoulos; Lars Schmidt-Thieme (ECML/PKDD (2), 2012)

Real-time top-n recommendation in social streams. - Ernesto Diaz-Aviles; Lucas Drumond; Lars Schmidt-Thieme; Wolfgang Nejdl (RecSys, 2012)

Exploiting the characteristics of matrix factorization for active learning in recommender systems. - Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme (RecSys, 2012)

Predicting RDF triples in incomplete knowledge bases with tensor factorization. - Lucas Drumond; Steffen Rendle; Lars Schmidt-Thieme (SAC, 2012)

Factorization techniques for student performance classification and ranking. - Lucas Drumond; Nguyen Thai-Nghe; Tomás Horváth; Lars Schmidt-Thieme (UMAP Workshops, 2012)

Using factorization machines for student modeling. - Nguyen Thai-Nghe; Lucas Drumond; Tomás Horváth; Lars Schmidt-Thieme (UMAP Workshops, 2012)

Multi-relational matrix factorization using bayesian personalized ranking for social network data. - Artus Krohn-Grimberghe; Lucas Drumond; Christoph Freudenthaler; Lars Schmidt-Thieme (WSDM, 2012)

GRAMOFON: General model-selection framework based on networks. - Krisztián Búza; Alexandros Nanopoulos; Tomás Horváth; Lars Schmidt-Thieme (Neurocomputing, 2012)

Personalized Ranking for Non-Uniformly Sampled Items. - Zeno Gantner; Lucas Drumond; Christoph Freudenthaler; Lars Schmidt-Thieme (KDD Cup, 2012)

Supervised Dimensionality Reduction via Nonlinear Target Estimation. - Josif Grabocka; Lucas Drumond; Lars Schmidt-Thieme (DaWaK, 2013)

Hough Transform and Kirchhoff Migration for Supervised GPR Data Analysis. - André Busche; Daniel Seyfried; Lars Schmidt-Thieme (ECDA, 2013)

P2P RVM for Distributed Classification. - Muhammad Umer Khan; Alexandros Nanopoulos; Lars Schmidt-Thieme (ECDA, 2013)

Event Prediction in Pharyngeal High-Resolution Manometry. - Nicolas Schilling; André Busche; Simone Miller; Michael Jungheim; Martin Ptok; Lars Schmidt-Thieme (ECDA, 2013)

HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (ICTAI, 2013)

Factorized Decision Trees for Active Learning in Recommender Systems. - Rasoul Karimi; Martin Wistuba; Alexandros Nanopoulos; Lars Schmidt-Thieme (ICTAI, 2013)

Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. - Martin Wistuba; Lars Schmidt-Thieme (KI, 2013)

Analysing the Potential Impact of Labeling Disagreements for Engineering Sensor Data. - André Busche; Ruth Janning; Lars Schmidt-Thieme (LWA, 2013)

Towards Optimal Active Learning for Matrix Factorization in Recommender Systems. - Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme (LWA, 2013)

Supervised Clustering of Social Media Streams. - Martin Wistuba; Lars Schmidt-Thieme (MediaEval, 2013)

Towards real-time collaborative filtering for big fast data. - Ernesto Diaz-Aviles; Wolfgang Nejdl; Lucas Drumond; Lars Schmidt-Thieme (WWW (Companion Volume), 2013)

Invariant Factorization Of Time-Series. - Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2013)

Time-Series Classification Through Histograms of Symbolic Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2013)

An Evaluation Measure for Learning from Imbalanced Data Based on Asymmetric Beta Distribution. - Nguyen Thai-Nghe; Zeno Gantner; Lars Schmidt-Thieme (Classification and Data Mining, 2013)

Optimizing Multi-Relational Factorization Models for Multiple Target Relations. - Lucas Rêgo Drumond; Ernesto Diaz-Aviles; Lars Schmidt-Thieme; Wolfgang Nejdl (CIKM, 2014)

Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach. - Carlotta Schatten; Ruth Janning; Lars Schmidt-Thieme (CSEDU (Selected Papers), 2014)

Adaptive Content Sequencing without Domain Information. - Carlotta Schatten; Lars Schmidt-Thieme (CSEDU (1), 2014)

Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (EC-TEL, 2014)

Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (EDM (Workshops), 2014)

Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems. - Carlotta Schatten; Ruth Janning; Manolis Mavrikis; Lars Schmidt-Thieme (EDM, 2014)

Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. - Carlotta Schatten; Martin Wistuba; Lars Schmidt-Thieme; Sergio Gutiérrez Santos (ICALT, 2014)

Analyzing cooperative lane change models for connected vehicles. - Muhammad Umer Khan; Pavlos Basaras; Lars Schmidt-Thieme; Alexandros Nanopoulos; Dimitrios Katsaros 001 (ICCVE, 2014)

An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme; Gerhard Backfried; Norbert Pfannerer (ICTAI, 2014)

Automatic Subclasses Estimation for a Better Classification with HNNP. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (ISMIS, 2014)

Realistic optimal policies for energy-efficient train driving. - Josif Grabocka; Alexandros Dalkalitsis; Athanasios Lois; Evangelos Katsaros; Lars Schmidt-Thieme (ITSC, 2014)

Learning time-series shapelets. - Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (KDD, 2014)

Local Feature Extractors Accelerating HNNP for Phoneme Recognition. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (KI, 2014)

Improved Questionnaire Trees for Active Learning in Recommender Systems. - Rasoul Karimi; Alexandros Nanopoulos; Lars Schmidt-Thieme (LWA, 2014)

Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification. - Lucas Rêgo Drumond; Lars Schmidt-Thieme; Christoph Freudenthaler; Artus Krohn-Grimberghe (PAKDD (1), 2014)

Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. - Josif Grabocka; Erind Bedalli; Lars Schmidt-Thieme (PAKDD (1), 2014)

Buried pipe localization using an iterative geometric clustering on GPR data. - Ruth Janning; André Busche; Tomás Horváth; Lars Schmidt-Thieme (Artif. Intell. Rev., 2014)

Invariant time-series factorization. - Josif Grabocka; Lars Schmidt-Thieme (Data Min. Knowl. Discov., 2014)

Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS. - Carlotta Schatten; Ruth Janning; Lars Schmidt-Thieme (AAAI, 2015)

Recognizing Perceived Task Difficulty from Speech, Pause Histograms. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (AIED Workshops, 2015)

Learning hyperparameter optimization initializations. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (DSAA, 2015)

Improving Automatic Affect Recognition on Low-Level Speech Features in Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (EC-TEL, 2015)

Recognizing Perceived Task Difficulty from Speech and Pause Histograms. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (EDM (Workshops), 2015)

How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (EDM, 2015)

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets. - Lydia Voß; Carlotta Schatten; Claudia Mazziotti; Lars Schmidt-Thieme (EDM, 2015)

Sequential Model-Free Hyperparameter Tuning. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ICDM, 2015)

Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. - Nicolas Schilling; Martin Wistuba; Lucas Drumond; Lars Schmidt-Thieme (ICTAI, 2015)

Multi-relational Factorization Models for Student Modeling in Intelligent Tutoring Systems. - Nguyen Thai-Nghe; Lars Schmidt-Thieme (KSE, 2015)

Comparing Prediction Models for Active Learning in Recommender Systems. - Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme (LWA, 2015)

Geo_ML @ MediaEval Placing Task 2015. - Nghia Duong-Trung; Martin Wistuba; Lucas Rêgo Drumond; Lars Schmidt-Thieme (MediaEval, 2015)

Hyperparameter Optimization with Factorized Multilayer Perceptrons. - Nicolas Schilling; Martin Wistuba; Lucas Drumond; Lars Schmidt-Thieme (ECML/PKDD (2), 2015)

Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ECML/PKDD (2), 2015)

Learning Data Set Similarities for Hyperparameter Optimization Initializations. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (MetaSel@PKDD/ECML, 2015)

Optimal Time-Series Motifs. - Josif Grabocka; Nicolas Schilling; Lars Schmidt-Thieme (CoRR, 2015)

Scalable Discovery of Time-Series Shapelets. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2015)

Ultra-Fast Shapelets for Time Series Classification. - Martin Wistuba; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2015)

Beyond Manual Tuning of Hyperparameters. - Frank Hutter; Jörg Lücke; Lars Schmidt-Thieme (Künstliche Intell., 2015)

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (IEEE Trans. Knowl. Data Eng., 2015)

Learning Through Non-linearly Supervised Dimensionality Reduction. - Josif Grabocka; Lars Schmidt-Thieme (Trans. Large Scale Data Knowl. Centered Syst., 2015)

A supervised active learning framework for recommender systems based on decision trees. - Rasoul Karimi; Alexandros Nanopoulos; Lars Schmidt-Thieme (User Model. User Adapt. Interact., 2015)

Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression. - Nghia Duong-Trung; Nicolas Schilling; Lars Schmidt-Thieme (CIKM, 2016)

Learning DTW-Shapelets for Time-Series Classification. - Mit Shah; Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (CODS, 2016)

Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF. - Rosa Tsegaye Aga; Lucas Drumond; Christian Wartena; Lars Schmidt-Thieme (COLING, 2016)

Student Progress Modeling with Skills Deficiency Aware Kalman Filters. - Carlotta Schatten; Lars Schmidt-Thieme (CSEDU (1), 2016)

Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems. - Carlotta Schatten; Lars Schmidt-Thieme (CSEDU (Selected Papers), 2016)

Hyperparameter Optimization Machines. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (DSAA, 2016)

Learning Thesaurus Relations from Distributional Features. - Rosa Tsegaye Aga; Christian Wartena; Lucas Drumond; Lars Schmidt-Thieme (LREC, 2016)

Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream. - Nghia Duong-Trung; Nicolas Schilling; Lucas Drumond; Lars Schmidt-Thieme (LWDA, 2016)

Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. - Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (ECML/PKDD (1), 2016)

Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ECML/PKDD (1), 2016)

Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems. - Ruth Janning; Carlotta Schatten; Lars Schmidt-Thieme (Int. J. Artif. Intell. Educ., 2016)

Multi-Relational Learning at Scale with ADMM. - Lucas Drumond; Ernesto Diaz-Aviles; Lars Schmidt-Thieme (CoRR, 2016)

Bank Card Usage Prediction Exploiting Geolocation Information. - Martin Wistuba; Nghia Duong-Trung; Nicolas Schilling; Lars Schmidt-Thieme (CoRR, 2016)

Fast classification of univariate and multivariate time series through shapelet discovery. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (Knowl. Inf. Syst., 2016)

Latent Time-Series Motifs. - Josif Grabocka; Nicolas Schilling; Lars Schmidt-Thieme (ACM Trans. Knowl. Discov. Data, 2016)

On Discovering the Number of Document Topics via Conceptual Latent Space. - Nghia Duong-Trung; Lars Schmidt-Thieme (CIKM, 2017)

Finding Hierarchy of Topics from Twitter Data. - Nghia Duong-Trung; Nicolas Schilling; Lars Schmidt-Thieme (LWDA, 2017)

Personalized Deep Learning for Tag Recommendation. - Hanh T. H. Nguyen; Martin Wistuba; Josif Grabocka; Lucas Rêgo Drumond; Lars Schmidt-Thieme (PAKDD (1), 2017)

Personalized Tag Recommendation for Images Using Deep Transfer Learning. - Hanh T. H. Nguyen; Martin Wistuba; Lars Schmidt-Thieme (ECML/PKDD (2), 2017)

Automatic Frankensteining: Creating Complex Ensembles Autonomously. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (SDM, 2017)

Channel masking for multivariate time series shapelets. - Dripta S. Raychaudhuri; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2017)

Collaborative SVM classification in scale-free peer-to-peer networks. - Muhammad Umer Khan; Lars Schmidt-Thieme; Alexandros Nanopoulos (Expert Syst. Appl., 2017)

Towards Distributed Pairwise Ranking using Implicit Feedback. - Mohsan Jameel; Nicolas Schilling; Lars Schmidt-Thieme (SIGIR, 2018)

NeuralWarp: Time-Series Similarity with Warping Networks. - Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2018)

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (Mach. Learn., 2018)

Directly Optimizing IoU for Bounding Box Localization. - Mofassir ul Islam Arif; Mohsan Jameel; Lars Schmidt-Thieme (ACPR (1), 2019)

Multi-step Forecasting via Multi-task Learning. - Shayan Jawed; Ahmed Rashed; Lars Schmidt-Thieme (IEEE BigData, 2019)

Gait Verification using Deep Learning with a Pairwise Loss. - Vijaya Krishna Yalavarthi; Josif Grabocka; Hareesh Mandalapu; Lars Schmidt-Thieme (BIOSIG, 2019)

Multi-Label Network Classification via Weighted Personalized Factorizations. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (ICAART (2), 2019)

Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (ICAART (Revised Selected Papers), 2019)

A Hybrid Convolutional Approach for Parking Availability Prediction. - Hadi Samer Jomaa; Josif Grabocka; Lars Schmidt-Thieme; Alexander Borek (IJCNN, 2019)

Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (KDD, 2019)

Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD. - Mohsan Jameel; Josif Grabocka; Mofassir ul Islam Arif; Lars Schmidt-Thieme (PKDD/ECML Workshops (1), 2019)

A Deep Multi-task Approach for Residual Value Forecasting. - Ahmed Rashed; Shayan Jawed; Jens Rehberg; Josif Grabocka; Lars Schmidt-Thieme; Andre Hintsches (ECML/PKDD (3), 2019)

Attribute-aware non-linear co-embeddings of graph features. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (RecSys, 2019)

Data-Driven Vehicle Trajectory Forecasting. - Shayan Jawed; Eya Boumaiza; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Learning Surrogate Losses. - Josif Grabocka; Randolf Scholz; Lars Schmidt-Thieme (CoRR, 2019)

In Hindsight: A Smooth Reward for Steady Exploration. - Hadi S. Jomaa; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Hyp-RL : Hyperparameter Optimization by Reinforcement Learning. - Hadi S. Jomaa; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Handwritten Amharic Character Recognition Using a Convolutional Neural Network. - Mesay Samuel Gondere; Lars Schmidt-Thieme; Abiot Sinamo Boltena; Hadi Samer Jomaa (CoRR, 2019)

Chameleon: Learning Model Initializations Across Tasks With Different Schemas. - Lukas Brinkmeyer; Rafael Rêgo Drumond; Randolf Scholz; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

An efficient evolutionary solution to the joint order batching - order picking planning problem. - Riccardo Lucato; Jonas K. Falkner; Lars Schmidt-Thieme (GECCO Companion, 2020)

A machine learning approach to infer on-street parking occupancy based on parking meter transactions. - Jonas Sonntag; Lars Schmidt-Thieme (ITSC, 2020)

Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks. - Mofassir ul Islam Arif; Mohsan Jameel; Josif Grabocka; Lars Schmidt-Thieme (LWDA, 2020)

Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD. - Mohsan Jameel; Shayan Jawed; Lars Schmidt-Thieme (PAKDD (2), 2020)

Self-supervised Learning for Semi-supervised Time Series Classification. - Shayan Jawed; Josif Grabocka; Lars Schmidt-Thieme (PAKDD (1), 2020)

Automation of Leasing Vehicle Return Assessment Using Deep Learning Models. - Mohsan Jameel; Mofassir ul Islam Arif; Andre Hintsches; Lars Schmidt-Thieme (ECML/PKDD (4), 2020)

MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. - Ahmed Rashed; Shayan Jawed; Lars Schmidt-Thieme; Andre Hintsches (RecSys, 2020)

HIDRA: Head Initialization across Dynamic targets for Robust Architectures. - Rafael Rêgo Drumond; Lukas Brinkmeyer; Josif Grabocka; Lars Schmidt-Thieme (SDM, 2020)

Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention. - Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2020)

Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning. - Jonas Sonntag; Michael Engel; Lars Schmidt-Thieme (AAAI, 2021)

Multimodal Meta-Learning for Time Series Regression. - Sebastian Pineda-Arango; Felix Heinrich; Kiran Madhusudhanan; Lars Schmidt-Thieme (AALTD@ECML/PKDD, 2021)

Training Object Detectors if Only Large Objects are Labeled. - Daniel Pototzky; Matthias Kirschner; Azhar Sultan; Lars Schmidt-Thieme (BMVC, 2021)

Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling. - Daniel Pototzky; Matthias Kirschner; Lars Schmidt-Thieme (GCPR, 2021)

Self-supervised Learning for Object Detection in Autonomous Driving. - Daniel Pototzky; Azhar Sultan; Matthias Kirschner; Lars Schmidt-Thieme (GCPR, 2021)

RP-DQN: An Application of Q-Learning to Vehicle Routing Problems. - Ahmad Bdeir; Simon Boeder; Tim Dernedde; Kirill Tkachuk; Jonas K. Falkner; Lars Schmidt-Thieme (KI, 2021)

Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets. - Shayan Jawed; Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (ECML/PKDD (1), 2021)

A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (SIGIR, 2021)

Do We Really Need Deep Learning Models for Time Series Forecasting? - Shereen Elsayed; Daniela Thyssens; Ahmed Rashed; Lars Schmidt-Thieme; Hadi Samer Jomaa (CoRR, 2021)

Hyperparameter Optimization with Differentiable Metafeatures. - Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (CoRR, 2021)

Multi-script Handwritten Digit Recognition Using Multi-task Learning. - Mesay Samuel Gondere; Lars Schmidt-Thieme; Durga Prasad Sharma; Randolf Scholz (CoRR, 2021)

Deep Metric Learning for Ground Images. - Raaghav Radhakrishnan; Jan Fabian Schmid; Randolf Scholz; Lars Schmidt-Thieme (CoRR, 2021)

Improving Hyperparameter Optimization by Planning Ahead. - Hadi S. Jomaa; Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2021)

Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. - Kiran Madhusudhanan; Johannes Burchert; Nghia Duong-Trung; Stefan Born; Lars Schmidt-Thieme (CoRR, 2021)

Dataset2Vec: learning dataset meta-features. - Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (Data Min. Knowl. Discov., 2021)

GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting. - Shayan Jawed; Lars Schmidt-Thieme (IEEE Big Data, 2022)

On the Potential of Using ERP Business and System Data for Fraud Detection. - Jonas Schnepf; Paula Vetter; Tarik Temel; Bernd Scheuermann; Lars Schmidt-Thieme (IEEE Big Data, 2022)

FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU. - Daniel Pototzky; Azhar Sultan; Lars Schmidt-Thieme (GCPR, 2022)

Positive-Unlabeled Domain Adaptation. - Jonas Sonntag; Gunnar Behrens; Lars Schmidt-Thieme (DSAA, 2022)

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (DSAA, 2022)

Zero-shot AutoML with Pretrained Models. - Ekrem Öztürk; Fabio Ferreira; Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka; Frank Hutter (ICML, 2022)

Parting With Illusions About Synthetic Data. - Daniel Pototzky; Azhar Sultan; Lars Schmidt-Thieme (IVMSP, 2022)

Does Self-Supervised Pretraining Really Match ImageNet Weights? - Daniel Pototzky; Azhar Sultan; Lars Schmidt-Thieme (IVMSP, 2022)

Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning. - Christian Löwens; Inaam Ashraf; Alexander Gembus; Genesis Cuizon; Jonas K. Falkner; Lars Schmidt-Thieme (KI, 2022)

Deep Level Annotation for Painter Attribution on Greek Vases utilizing Object Detection. - Marta Kipke; Lukas Brinkmeyer; Souaybou Bagayoko; Lars Schmidt-Thieme; Martin Langner (SUMAC @ ACM Multimedia, 2022)

Open Set Recognition for Time Series Classification. - Tolga Akar; Thorben Werner; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (PAKDD (2), 2022)

Offline Handwritten Amharic Character Recognition Using Few-Shot Learning. - Mesay Samuel Samuel; Lars Schmidt-Thieme; Durga Prasad Sharma; Abiot Sinamo Boltena; Abey Bruck (PanAfriCon AI, 2022)

Attention, Filling in the Gaps for Generalization in Routing Problems. - Ahmad Bdeir; Jonas K. Falkner; Lars Schmidt-Thieme (ECML/PKDD (6), 2022)

Few-Shot Forecasting of Time-Series with Heterogeneous Channels. - Lukas Brinkmeyer; Rafael Rêgo Drumond; Johannes Burchert; Lars Schmidt-Thieme (ECML/PKDD (6), 2022)

Learning to Control Local Search for Combinatorial Optimization. - Jonas K. Falkner; Daniela Thyssens; Ahmad Bdeir; Lars Schmidt-Thieme (ECML/PKDD (5), 2022)

U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. - Kiran Madhusudhanan; Johannes Burchert; Nghia Duong-Trung; Stefan Born; Lars Schmidt-Thieme (ECML/PKDD (6), 2022)

Context and Attribute-Aware Sequential Recommendation via Cross-Attention. - Ahmed Rashed; Shereen Elsayed; Lars Schmidt-Thieme (RecSys, 2022)

Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size. - Daniela Thyssens; Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2022)

A.I. and Data-Driven Mobility at Volkswagen Financial Services AG. - Shayan Jawed; Mofassir ul Islam Arif; Ahmed Rashed; Kiran Madhusudhanan; Shereen Elsayed; Mohsan Jameel; Alexei Volk; Andre Hintsches; Marlies Kornfeld; Katrin Lange; Lars Schmidt-Thieme (CoRR, 2022)

Improving Amharic Handwritten Word Recognition Using Auxiliary Task. - Mesay Samuel Gondere; Lars Schmidt-Thieme; Durga Prasad Sharma; Abiot Sinamo Boltena (CoRR, 2022)

CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention. - Ahmed Rashed; Shereen Elsayed; Lars Schmidt-Thieme (CoRR, 2022)

Large Neighborhood Search based on Neural Construction Heuristics. - Jonas K. Falkner; Daniela Thyssens; Lars Schmidt-Thieme (CoRR, 2022)

End-to-End Image-Based Fashion Recommendation. - Shereen Elsayed; Lukas Brinkmeyer; Lars Schmidt-Thieme (CoRR, 2022)

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. - Nghia Duong-Trung; Stefan Born; Jong Woo Kim; Marie-Therese Schermeyer; Katharina Paulick; Maxim Borisyak; Ernesto C. Martínez; Mariano Nicolás Cruz Bournazou; Thorben Werner; Randolf Scholz; Lars Schmidt-Thieme; Peter Neubauer (CoRR, 2022)

Offline Handwritten Amharic Character Recognition Using Few-shot Learning. - Mesay Samuel Gondere; Lars Schmidt-Thieme; Durga Prasad Sharma; Abiot Sinamo Boltena; Abey Bruck (CoRR, 2022)

Tripletformer for Probabilistic Interpolation of Asynchronous Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (CoRR, 2022)

Auxiliary Quantile Forecasting with Linear Networks. - Shayan Jawed; Lars Schmidt-Thieme (CoRR, 2022)

Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series. - Nghia Duong-Trung; Stefan Born; Kiran Madhusudhanan; Randolf Scholz; Johannes Burchert; Danh Le Phuoc; Lars Schmidt-Thieme (CoRR, 2022)

Pricing Used Vehicles at Volkswagen Financial Services AG. - Shayan Jawed; Jan Stening; Lars Schmidt-Thieme (IEEE Big Data, 2023)

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (IEEE Big Data, 2023)

Neural Capacitated Clustering. - Jonas K. Falkner; Lars Schmidt-Thieme (IJCAI, 2023)

Deep Multi-Representation Model for Click-Through Rate Prediction. - Shereen Elsayed; Lars Schmidt-Thieme (IJCNN, 2023)

Forecasting Early with Meta Learning. - Shayan Jawed; Kiran Madhusudhanan; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (IJCNN, 2023)

DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision. - Christian Löwens; Daniela Thyssens; Emma Andersson; Christina Jenkins; Lars Schmidt-Thieme (ITSC, 2023)

Few-Shot Human Motion Prediction for Heterogeneous Sensors. - Rafael Rêgo Drumond; Lukas Brinkmeyer; Lars Schmidt-Thieme (PAKDD (2), 2023)

ConvMix: Combining Intermediate Latent Features in Deep Convolutional Neural Networks. - Mofassir ul Islam Arif; Johannes Burchert; Lars Schmidt-Thieme (SCIA (2), 2023)

Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks. - Mofassir ul Islam Arif; Mohsan Jameel; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2023)

Forecasting Irregularly Sampled Time Series using Graphs. - Vijaya Krishna Yalavarthi; Kiran Madusudanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (CoRR, 2023)

Too Big, so Fail? - Enabling Neural Construction Methods to Solve Large-Scale Routing Problems. - Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2023)

Routing Arena: A Benchmark Suite for Neural Routing Solvers. - Daniela Thyssens; Tim Dernedde; Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2023)

Context-Aware Sequential Model for Multi-Behaviour Recommendation. - Shereen Elsayed; Ahmed Rashed; Lars Schmidt-Thieme (CoRR, 2023)

Sparse self-attention guided generative adversarial networks for time-series generation. - Nourhan Ahmed; Lars Schmidt-Thieme (Int. J. Data Sci. Anal., 2023)

GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. - Vijaya Krishna Yalavarthi; Kiran Madhusudhanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (AAAI, 2024)

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing. - Kiran Madhusudhanan; Gunnar Behrens; Maximilian Stubbemann; Lars Schmidt-Thieme (IEEE Big Data, 2024)

A Cross-Domain Benchmark for Active Learning. - Thorben Werner; Johannes Burchert; Maximilian Stubbemann; Lars Schmidt-Thieme (NeurIPS, 2024)

HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation. - Shereen Elsayed; Ahmed Rashed; Lars Schmidt-Thieme (PAKDD (5), 2024)

Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting. - Kiran Madhusudhanan; Shayan Jawed; Lars Schmidt-Thieme (PAKDD (6), 2024)

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. - Christian Klötergens; Vijaya Krishna Yalavarthi; Maximilian Stubbemann; Lars Schmidt-Thieme (ECML/PKDD (4), 2024)

Multi-Behavioral Sequential Recommendation. - Shereen Elsayed; Ahmed Rashed; Lars Schmidt-Thieme (RecSys, 2024)

Learning Set Embeddings for Fashion Compatibility Recommendation. - Indra Firmansyah; Randolf Scholz; Adrian Nahmendorff; Ngoc Son Le; Shereen Elsayed; Lars Schmidt-Thieme (SURE@RecSys, 2024)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting. - Kiran Madhusudhanan; Shayan Jawed; Lars Schmidt-Thieme (CoRR, 2024)

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. - Johannes Burchert; Thorben Werner; Vijaya Krishna Yalavarthi; Diego Coello de Portugal; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2024)

Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. - Vijaya Krishna Yalavarthi; Randolf Scholz; Kiran Madhusudhanan; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Learning attentive attribute-aware node embeddings in dynamic environments. - Nourhan Ahmed; Ahmed Rashed; Lars Schmidt-Thieme (Int. J. Data Sci. Anal., 2024)

Structure-aware decoupled imputation network for multivariate time series. - Nourhan Ahmed; Lars Schmidt-Thieme (Data Min. Knowl. Discov., 2024)

Motif-aware Graph Neural Networks for Networked Time Series Imputation. - Nourhan Ahmed; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (AAAI, 2025)

Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (AAAI, 2025)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. - Christian Klötergens; Vijaya Krishna Yalavarthi; Randolf Scholz; Maximilian Stubbemann; Stefan Born; Lars Schmidt-Thieme (ICLR, 2025)

Moco: A Learnable Meta Optimizer for Combinatorial Optimization. - Tim Dernedde; Daniela Thyssens; Sören Dittrich; Maximilian Stubbemann; Lars Schmidt-Thieme (PAKDD (3), 2025)

Bayesian Active Learning By Distribution Disagreement. - Thorben Werner; Lars Schmidt-Thieme (CoRR, 2025)

Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting? - Ibram Abdelmalak; Kiran Madhusudhanan; Jungmin Choi; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2025)

IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting. - Christian Klötergens; Tim Dernedde; Lars Schmidt-Thieme (CoRR, 2025)

STADE: Standard Deviation as a Pruning Metric. - Diego Coello de Portugal Mecke; Haya Alyoussef; Ilia Koloiarov; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2025)

The Role of Active Learning in Modern Machine Learning. - Thorben Werner; Lars Schmidt-Thieme; Vijaya Krishna Yalavarthi (CoRR, 2025)

On Distributional Dependent Performance of Classical and Neural Routing Solvers. - Daniela Thyssens; Tim Dernedde; Wilson Sentanoe; Lars Schmidt-Thieme (CoRR, 2025)