Lars Schmidt-Thieme

Lars Schmidt-Thieme, Alexandros Nanopoulos, Christoph Freudenthaler, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

Realistic optimal policies for energy-efficient train driving

Lars Schmidt-Thieme, Josif Grabocka, Athanasios Lois, 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

Factorization Techniques for Predicting Student Performance

Lars Schmidt-Thieme, Alexandros Nanopoulos, Artus Krohn-Grimberghe, 2012 .

Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques

Lars Schmidt-Thieme, Philipp Cimiano, Lucas Drumond, 2011, ICWSM.

Real World Social Tagging Recommender Systems

Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis, 2012 .

Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference

Lars Schmidt-Thieme, Steffen Rendle, 2008, 2008 Eighth IEEE International Conference on Data Mining.

HIDRA: Head Initialization across Dynamic targets for Robust Architectures

Lars Schmidt-Thieme, Josif Grabocka, Rafael Rego Drumond, 2020, SDM.

Learning taxonomic relations from heterogeneous sources

Steffen Staab, Lars Schmidt-Thieme, Philipp Cimiano, 2004 .

Tag Recommendations in Folksonomies

Andreas Hotho, Gerd Stumme, Lars Schmidt-Thieme, 2007, LWA.

Recommender Systems for Social Tagging Systems

Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis, 2012, SpringerBriefs in Electrical and Computer Engineering.

Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems

Lars Schmidt-Thieme, Sergio Gutiérrez Santos, Carlotta Schatten, 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

Handwritten Amharic Character Recognition Using a Convolutional Neural Network pdf

Lars Schmidt-Thieme, Abiot Sinamo Boltena, Hadi Samer Jomaa, 2019, ArXiv.

Programming in Prolog ("A Short Tutorial")

Lars Schmidt-Thieme, Krisztian Buza, 2008 .

Visualizing Recommender System Results via Multidimensional Scaling

Lars Schmidt-Thieme, Wolfgang Gaul, Patrick Thoma, 2004 .

Pairwise interaction tensor factorization for personalized tag recommendation

Lars Schmidt-Thieme, Steffen Rendle, L. Schmidt-Thieme, 2010, WSDM '10.

Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream

Lars Schmidt-Thieme, Nicolas Schilling, Nghia Duong-Trung, 2016, LWDA.

Individualized Error Estimation for Classification and Regression Models

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2010, GfKl.

XML and Semantic Web Technologies I. XML / 5. XML Stylesheet Language Transformations (XSLT)

Lars Schmidt-Thieme, 2012 .

Data Analysis, Machine Learning and Knowledge Discovery - Proceedings of the 36th Annual Conference of the Gesellschaft für Klassifikation e. V., Hildesheim, Germany, August 2012

Gesellschaft für Klassifikation. Jahrestagung, Lars Schmidt-Thieme, Ruth Janning, 2014, GfKl.

Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks

Lars Schmidt-Thieme, Josif Grabocka, Mohsan Jameel, 2020, LWDA.

Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data

Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, 2006, Data Science and Classification.

Comparing Prediction Models for Active Learning in Recommender Systems

Lars Schmidt-Thieme, Alexandros Nanopoulos, Christoph Freudenthaler, 2015, LWA.

Social Tagging Systems

Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis, 2012 .

Java : eine Einführung ; [100 % Swing]

Martin Schader, Lars Schmidt-Thieme, 2000 .

Relational Ensemble Classification

Lars Schmidt-Thieme, Christine Preisach, L. Schmidt-Thieme, 2006, Sixth International Conference on Data Mining (ICDM'06).

Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems

Lars Schmidt-Thieme, Carlotta Schatten, L. Schmidt-Thieme, 2016, CSEDU.

Beyond Manual Tuning of Hyperparameters

Lars Schmidt-Thieme, Frank Hutter, Jörg Lücke, 2015, KI - Künstliche Intelligenz.

Invariant time-series factorization

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2014, Data Mining and Knowledge Discovery.

Factorizing Markov Models for Categorical Time Series Prediction

Lars Schmidt-Thieme, Steffen Rendle, Christoph Freudenthaler, 2011 .

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2011, HAIS.

Cross-tagging for personalized open social networking

Avare Stewart, Lars Schmidt-Thieme, Alexandros Nanopoulos, 2009, HT '09.

Attribute-aware Collaborative Filtering

Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, L. Schmidt-Thieme, 2005, GfKl.

Adaptive Content Sequencing without Domain Information

Lars Schmidt-Thieme, Carlotta Schatten, L. Schmidt-Thieme, 2014, CSEDU.

Requirement Analysis of Uncertainty in the Management of Multimedia Objects in X-Media - X-Media Internal Deliverable ID2.1

Steffen Staab, Lars Schmidt-Thieme, Philipp Cimiano, 2006 .

Challenges at the Interface of Data Analysis, Computer Science, and Optimization: Proceedings of the 34th Annual Conference of the Gesellschaft fur Data Analysis, and Knowledge Organization

Lars Schmidt-Thieme, Wolfgang A. Gaul, Jonas Kunze, 2012 .

Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, ICAART.

Information Systems 2

Lars Schmidt-Thieme, L. Schmidt-Thieme, 2008 .

Factorized Decision Trees for Active Learning in Recommender Systems

Lars Schmidt-Thieme, Martin Wistuba, Rasoul Karimi, 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.

How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2015, EDM.

Active Learning for Technology Enhanced Learning

Lars Schmidt-Thieme, Alexandros Nanopoulos, Artus Krohn-Grimberghe, 2011, LWA.

RFID-Enhanced Museum for Interactive Experience

Lars Schmidt-Thieme, Alexandros Nanopoulos, Rasoul Karimi, 2011, MM4CH.

1. Basic Probability Calculus

Lars Schmidt-Thieme, 2010 .

Collaborative Tag Recommendations

Lars Schmidt-Thieme, Leandro Balby Marinho, L. Schmidt-Thieme, 2007, GfKl.

Data-Driven Vehicle Trajectory Forecasting pdf

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2019, ArXiv.

XML and Semantic Web Technologies II. XML / 2. XML Document Type Definitions (DTDs)

Lars Schmidt-Thieme, 2012 .

Data Analysis, Machine Learning and Applications: Proceedings of the 31st Annual Conference of the Gesellschaft fr Klassifikation e.V., Albert-Ludwigs-Universitt ... Data Analysis, and Knowledge Organization)

Lars Schmidt-Thieme, Hans Burkhardt, Reinhold Decker, 2008 .

A Unifying Framework for GPR Image Reconstruction

Lars Schmidt-Thieme, Ruth Janning, André Busche, 2012, GfKl.

Factorizing personalized Markov chains for next-basket recommendation

Lars Schmidt-Thieme, Steffen Rendle, Christoph Freudenthaler, 2010, WWW '10.

In Hindsight: A Smooth Reward for Steady Exploration pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, ArXiv.

Optimizing Multi-Relational Factorization Models for Multiple Target Relations

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Lucas Drumond, 2014, CIKM.

Data Analysis, Machine Learning and Knowledge Discovery (Studies in Classification, Data Analysis, and Knowledge Organization)

Lars Schmidt-Thieme, Ruth Janning, Myra Spiliopoulou, 2013 .

Bayesian Personalized Ranking for Non-Uniformly Sampled Items

Lars Schmidt-Thieme, Zeno Gantner, Lucas Drumond, 2011 .

On benchmarking frequent itemset mining algorithms: from measurement to analysis

Lars Schmidt-Thieme, Ferenc Bodon, Balázs Rácz, 2005 .

Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2016, International Journal of Artificial Intelligence in Education.

Supervised Nonlinear Factorizations Excel In Semi-supervised Regression

Lars Schmidt-Thieme, Josif Grabocka, Erind Bedalli, 2014, PAKDD.

P2P RVM for Distributed Classification

Lars Schmidt-Thieme, Alexandros Nanopoulos, Muhammad Umer Khan, 2013, ECDA.

Combining multi-distributed mixture models and bayesian networks for semi-supervised learning

Lars Schmidt-Thieme, Manuel Stritt, Gerhard Poeppel, 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

Improved Questionnaire Trees for Active Learning in Recommender Systems

Lars Schmidt-Thieme, Alexandros Nanopoulos, Rasoul Karimi, 2014, LWA.

Learning time-series shapelets

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2014, KDD.

Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems

Manolis Mavrikis, Lars Schmidt-Thieme, Ruth Janning, 2014, EDM.

What is happening right now ... that interests me?: online topic discovery and recommendation in twitter

Lars Schmidt-Thieme, Zeno Gantner, Ernesto Diaz-Aviles, 2012, CIKM.

Data Analysis and Decision Support (Studies in Classification, Data Analysis, and Knowledge Organization)

Lars Schmidt-Thieme, Daniel Baier, Reinhold Decker, 2005 .

Factorization models for context-/time-aware movie recommendations

Lars Schmidt-Thieme, Zeno Gantner, Steffen Rendle, 2010 .

Mining Web Navigation Path Fragments

Lars Schmidt-Thieme, Wolfgang Gaul, 2002 .

Do We Really Need Deep Learning Models for Time Series Forecasting? pdf

Lars Schmidt-Thieme, Hadi Samer Jomaa, Daniela Thyssens, 2021, ArXiv.

Ontology-Based Query Refinement for Semantic Portals

Lars Schmidt-Thieme, Rudi Studer, Nenad Stojanovic, 2005, From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments.

Learning Data Set Similarities for Hyperparameter Optimization Initializations

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2015, MetaSel@PKDD/ECML.

6. Exact Inference / Examples

Lars Schmidt-Thieme, 2010 .

GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing

Lars Schmidt-Thieme, Ruth Janning, André Busche, 2012, AIAI.

Hyperparameter Optimization with Factorized Multilayer Perceptrons

Lars Schmidt-Thieme, Nicolas Schilling, Lucas Drumond, 2015, ECML/PKDD.

Chameleon: Learning Model Initializations Across Tasks With Different Schemas pdf

Lars Schmidt-Thieme, Josif Grabocka, Randolf Scholz, 2019, ArXiv.

Reservation Price Estimation by Adaptive Conjoint Analysis

Lars Schmidt-Thieme, Michael Hahsler, Christoph Breidert, 2004, GfKl.

Performance Drivers for Depth-First Frequent Pattern Mining

Martin Schader, Lars Schmidt-Thieme, M. Schader, 2005, Data Analysis and Decision Support.

Latent Time-Series Motifs

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2016, ACM Trans. Knowl. Discov. Data.

Matrix and Tensor Factorization for Predicting Student Performance

Lars Schmidt-Thieme, Alexandros Nanopoulos, Nguyen Thai-Nghe, 2011, CSEDU.

Relation Extraction for the Semantic Web with Taxonomic Sequential Patterns

Lars Schmidt-Thieme, Philipp Cimiano, Krisztian Buza, 2011 .

Personalized Forecasting Student Performance

Lars Schmidt-Thieme, Nguyen Thai-Nghe, Tomás Horváth, 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

Dataset2Vec: learning dataset meta-features pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, Data Mining and Knowledge Discovery.

MyMediaLite: a free recommender system library

Lars Schmidt-Thieme, Zeno Gantner, Steffen Rendle, 2011, RecSys '11.

Recommender system for predicting student performance

Lars Schmidt-Thieme, Artus Krohn-Grimberghe, Nguyen Thai-Nghe, 2010, RecSysTEL@RecSys.

Self-supervised Learning for Semi-supervised Time Series Classification

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2020, PAKDD.

Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2014, EC-TEL.

4. Exact Inference / Variable Elimination

Lars Schmidt-Thieme, 2013 .

CNN-FM: Personalized Content-Aware Image Tag Recommendation

Lars Schmidt-Thieme, Martin Wistuba, Hanh T. H. Nguyen, 2017 .

3. Distributed Information Systems I: CORBA

Lars Schmidt-Thieme, L. Schmidt-Thieme, 2012 .

Workshop on user-centric evaluation of recommender systems and their interfaces

Lars Schmidt-Thieme, Bart P. Knijnenburg, Dirk G. F. M. Bollen, 2010, RecSys.

Learning Taxonomic Relations from Heterogeneous Evidence

Steffen Staab, Lars Schmidt-Thieme, Philipp Cimiano, 2004 .

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, IEEE Transactions on Knowledge and Data Engineering.

Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics

Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, L. Schmidt-Thieme, 2006, PAKDD.

Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons

Lars Schmidt-Thieme, Nicolas Schilling, Lucas Drumond, 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

Compound classification models for recommender systems

Lars Schmidt-Thieme, L. Schmidt-Thieme, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Analysing the Potential Impact of Labeling Disagreements for Engineering Sensor Data

Lars Schmidt-Thieme, Ruth Janning, André Busche, 2013, LWA.

Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2015, ECML/PKDD.

Finding Hierarchy of Topics from Twitter Data

Lars Schmidt-Thieme, Nicolas Schilling, Nghia Duong-Trung, 2017, LWDA.

An Effective Approach for Geolocation Prediction in Twitter Streams Using Clustering Based Discretization

Lars Schmidt-Thieme, Nicolas Schilling, Nghia Duong-Trung, 2017 .

Tag recommendations in social bookmarking systems

Andreas Hotho, Gerd Stumme, Lars Schmidt-Thieme, 2008, AI Commun..

Personalized Ranking for Non-Uniformly Sampled Items

Lars Schmidt-Thieme, Zeno Gantner, Lucas Drumond, 2012, KDD Cup.

Hough Transform and Kirchhoff Migration for Supervised GPR Data Analysis

Lars Schmidt-Thieme, André Busche, Daniel Seyfried, 2013, ECDA.

Multi-step Forecasting via Multi-task Learning

Lars Schmidt-Thieme, Shayan Jawed, Ahmed Rashed, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF

Lars Schmidt-Thieme, Christian Wartena, Lucas Drumond, 2016, COLING.

Time-Series Classification Based on Individualised Error Prediction

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2010, 2010 13th IEEE International Conference on Computational Science and Engineering.

Ultra-Fast Shapelets for Time Series Classification pdf

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, ArXiv.

A Hybrid Convolutional Approach for Parking Availability Prediction

Lars Schmidt-Thieme, Josif Grabocka, Alexander Borek, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Fast Classification of Electrocardiograph Signals via Instance Selection

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

Real-time top-n recommendation in social streams

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Lucas Drumond, 2012, RecSys.

Exploiting the characteristics of matrix factorization for active learning in recommender systems

Lars Schmidt-Thieme, Alexandros Nanopoulos, Christoph Freudenthaler, 2012, RecSys.

A supervised active learning framework for recommender systems based on decision trees

Lars Schmidt-Thieme, Alexandros Nanopoulos, Rasoul Karimi, 2015, User Modeling and User-Adapted Interaction.

GRAMOFON: General model-selection framework based on networks

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2012, Neurocomputing.

Attribute-aware non-linear co-embeddings of graph features

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, RecSys.

Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2015, AAAI.

Proceedings of the third ACM conference on Recommender systems

Lars Schmidt-Thieme, Robin Burke, Alexander Felfernig, 2008, RecSys 2008.

NeuralWarp: Time-Series Similarity with Warping Networks pdf

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2018, ArXiv.

A machine learning approach to infer on-street parking occupancy based on parking meter transactions

Lars Schmidt-Thieme, Jonas Sonntag, 2020, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

Swarming to rank for information retrieval

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Wolfgang Nejdl, 2009, GECCO.

Local Feature Extractors Accelerating HNNP for Phoneme Recognition

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2014, KI.

Sequential Model-Free Hyperparameter Tuning

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2015, 2015 IEEE International Conference on Data Mining.

Optimal Discretization of Quantitative Attributes for Association Rules

Lars Schmidt-Thieme, Stefan Born, L. Schmidt-Thieme, 2004 .

INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2011, PAKDD.

Efficient Classification of Long Time-Series

Lars Schmidt-Thieme, Josif Grabocka, Erind Bedalli, 2012, ICT Innovations.

Hyp-RL : Hyperparameter Optimization by Reinforcement Learning pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, ArXiv.

Multi-Label Network Classification via Weighted Personalized Factorizations pdf

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, ICAART.

Scalable Discovery of Time-Series Shapelets pdf

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, ArXiv.

Tag-aware recommender systems by fusion of collaborative filtering algorithms

Lars Schmidt-Thieme, Leandro Balby Marinho, Karen H. L. Tso-Sutter, 2008, SAC '08.

Frequent Generalized Subsequences — A Problem From Web Mining

Wolfgang Gaul, Lars Schmidt-Thieme, 2000 .

Automatic Bookmark Classification: A Collaborative Approach

Dominik Benz, Lars Schmidt-Thieme, Karen H. L. Tso, 2006 .

Multinomial SVM Item Recommender for Repeat-Buying Scenarios

Lars Schmidt-Thieme, Christina Lichtenthäler, L. Schmidt-Thieme, 2012, GfKl.

Hyperparameter Optimization Machines

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Factor Models for Tag Recommendation in BibSonomy

Lars Schmidt-Thieme, Steffen Rendle, L. Schmidt-Thieme, 2009, DC@PKDD/ECML.

Towards real-time collaborative filtering for big fast data

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Lucas Drumond, 2013, WWW.

Improving Academic Performance Prediction by Dealing with Class Imbalance

Lars Schmidt-Thieme, Nguyen Thai-Nghe, André Busche, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

Factorization forecasting approach for user modeling

Lars Schmidt-Thieme, Nguyễn Thái Nghe, L. Schmidt-Thieme, 2015 .

Multi-relational Factorization Models for Student Modeling in Intelligent Tutoring Systems

Lars Schmidt-Thieme, Nguyen Thai-Nghe, 2015, 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE).

Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression

Lars Schmidt-Thieme, Nicolas Schilling, Nghia Duong-Trung, 2016, CIKM.

Move Prediction in Go - Modelling Feature Interactions Using Latent Factors

Lars Schmidt-Thieme, Martin Wistuba, 2013, KI.

Hyperparameter Optimization Across Problem Tasks

Lars Schmidt-Thieme, Nicolas Schilling, Torben Windler, 2018 .

HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.

Predicting RDF triples in incomplete knowledge bases with tensor factorization

Lars Schmidt-Thieme, Steffen Rendle, Lucas Drumond, 2012, SAC '12.

Relational Classification for Personalized Tag Recommendation

Lars Schmidt-Thieme, Leandro Balby Marinho, Christine Preisach, 2009, DC@PKDD/ECML.

Learning Optimal Threshold on Resampling Data to Deal with Class Imbalance

Lars Schmidt-Thieme, Nguyen Thai-Nghe, L. Schmidt-Thieme, 2010 .

Guest Editors' Introduction: Recommender Systems

Lars Schmidt-Thieme, Alexander Felfernig, Gerhard Friedrich, 2007, IEEE Intell. Syst..

Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007

Lars Schmidt-Thieme, Hans Burkhardt, Reinhold Decker, 2008, GfKl.

Gait Verification using Deep Learning with a Pairwise Loss

Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi, Josif Grabocka, 2019, 2019 International Conference of the Biometrics Special Interest Group (BIOSIG).

Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems pdf

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Cai-Nicolas Ziegler, 2008, ArXiv.

4. Distributed Information Systems II: Web Services

Lars Schmidt-Thieme, 2012 .

Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2014, CSEDU.

XML and Semantic Web Technologies / 1. Constraining Document Structure

Lars Schmidt-Thieme, 2009 .

Attribute Aware Anonymous Recommender Systems

Lars Schmidt-Thieme, Manuel Stritt, Karen H. L. Tso-Sutter, 2006, GfKl.

Social Tagging Recommender Systems

Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis, 2011, Recommender Systems Handbook.

MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems

Lars Schmidt-Thieme, Shayan Jawed, Ahmed Rashed, 2020, RecSys.

I. Bayesian Networks / 3. Parameter Learning with Missing Values

Wolfram Burgard, Luc De Raedt, Bernhard Nebel, 2005 .

XMedia: Web People Search by Clustering with Machinely Learned Similarity Measures

Lars Schmidt-Thieme, Lorenza Romano, Krisztian Buza, 2009 .

XML and Semantic Web Technologies

Lars Schmidt-Thieme, 2012 .

Learning Through Non-linearly Supervised Dimensionality Reduction

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2015, Trans. Large Scale Data Knowl. Centered Syst..

Bank Card Usage Prediction Exploiting Geolocation Information pdf

Lars Schmidt-Thieme, Nicolas Schilling, Nghia Duong-Trung, 2016, ArXiv.

Collaborative and Usage-driven Evolution of Personal Ontologies

Peter Haase, York Sure-Vetter, Andreas Hotho, 2005, LWA.

7. Approximate Inference / Sampling

Lars Schmidt-Thieme, 2010 .

Automatic Frankensteining: Creating Complex Ensembles Autonomously

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2017, SDM.

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2017, Machine Learning.

Pipe localization by apex detection

Lars Schmidt-Thieme, Ruth Janning, Andre Busche, 2012 .

Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2014, EDM.

Multi-Relational Learning at Scale with ADMM pdf

Lars Schmidt-Thieme, Ernesto Diaz-Aviles, Lucas Drumond, 2016, ArXiv.

Object Identification with Constraints

Lars Schmidt-Thieme, Steffen Rendle, L. Schmidt-Thieme, 2006, Sixth International Conference on Data Mining (ICDM'06).

9. Parameter Learning / MLE and MAP

Lars Schmidt-Thieme, 2010 .

An Evaluation Measure for Learning from Imbalanced Data Based on Asymmetric Beta Distribution

Lars Schmidt-Thieme, Nguyen Thai-Nghe, Zeno Gantner, 2013, Classification and Data Mining.

5. Exact Inference / Clustering

Lars Schmidt-Thieme, 2010 .

2. Modelling Information Systems II: XML

Lars Schmidt-Thieme, 2008 .

Automatic Content-Based Categorization of Wikipedia Articles

Lars Schmidt-Thieme, Zeno Gantner, L. Schmidt-Thieme, 2009, PWNLP@IJCNLP.

Assoziationsregel-Algorithmen für Daten mit komplexer Struktur: mit Anwendungen im Web Mining

Lars Schmidt-Thieme, 2003 .

A bottom-up approach

Rolf Backofen, Lars Schmidt-Thieme, Andreas W. Liehr, 2016 .

Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD

Lars Schmidt-Thieme, Josif Grabocka, Mohsan Jameel, 2019, PKDD/ECML Workshops.

Fast classification of univariate and multivariate time series through shapelet discovery

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2016, Knowledge and Information Systems.

Advanced AI Techniques I. Bayesian Networks / 1. Probabilistic Independence and Separation in Graphs

Wolfram Burgard, Luc De Raedt, Bernhard Nebel, 2005 .

1. Modelling Information Systems I: Databases

Lars Schmidt-Thieme, L. Schmidt-Thieme, 2008 .

On Discovering the Number of Document Topics via Conceptual Latent Space

Lars Schmidt-Thieme, Nghia Duong-Trung, L. Schmidt-Thieme, 2017, CIKM.

Towards Distributed Pairwise Ranking using Implicit Feedback

Lars Schmidt-Thieme, Nicolas Schilling, Mohsan Jameel, 2018, SIGIR.

Learning optimal ranking with tensor factorization for tag recommendation

Lars Schmidt-Thieme, Alexandros Nanopoulos, Leandro Balby Marinho, 2009, KDD.

Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Lars Schmidt-Thieme, Krisztian Buza, L. Schmidt-Thieme, 2008, GfKl.

BPR: Bayesian Personalized Ranking from Implicit Feedback pdf

Lars Schmidt-Thieme, Zeno Gantner, Steffen Rendle, 2009, UAI.

An efficient evolutionary solution to the joint order batching - order picking planning problem

Lars Schmidt-Thieme, Riccardo Lucato, Jonas Falkner, 2020, GECCO Companion.

Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem

Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, Stefan Hauger, 2007, GfKl.

Factorization Models for Forecasting Student Performance

Lars Schmidt-Thieme, Nguyen Thai-Nghe, Tomás Horváth, 2011, EDM.

Combining multi-distributed mixture models and bayesian networks for semi-supervised learning

Lars Schmidt-Thieme, Manuel Stritt, G. Poeppel, 2007, ICMLA 2007.

Scaling Record Linkage to Non-uniform Distributed Class Sizes

Lars Schmidt-Thieme, Steffen Rendle, 2008, PAKDD.

A Deep Multi-task Approach for Residual Value Forecasting

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2019, ECML/PKDD.

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, L. Schmidt-Thieme, 2006, J. Comput..

Supervised Clustering of Social Media Streams

Lars Schmidt-Thieme, Martin Wistuba, 2013, MediaEval.

IQ estimation for accurate time-series classification

Lars Schmidt-Thieme, Alexandros Nanopoulos, Krisztian Buza, 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Keyword-Based TV Program Recommendation

Lars Schmidt-Thieme, Zeno Gantner, Christian Wartena, 2011, ITWP@IJCAI.

Supporting collaborative hierarchical classification: Bookmarks as an example

Dominik Benz, Lars Schmidt-Thieme, Karen H. L. Tso-Sutter, 2007, Comput. Networks.

Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification

Lars Schmidt-Thieme, Artus Krohn-Grimberghe, Lucas Drumond, 2014, PAKDD.

A Novel Multidimensional Framework for Evaluating Recommender Systems

Lars Schmidt-Thieme, Alexandros Nanopoulos, Artus Krohn-Grimberghe, 2010, LWA.

Using factorization machines for student modeling

Lars Schmidt-Thieme, Nguyen Thai-Nghe, Lucas Drumond, 2012, UMAP Workshops.

Learning Surrogate Losses pdf

Lars Schmidt-Thieme, Josif Grabocka, Randolf Scholz, 2019, ArXiv.

Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Lars Schmidt-Thieme, Steffen Rendle, L. Schmidt-Thieme, 2008, RecSys '08.

Recognizing Perceived Task Difficulty from Speech and Pause Histograms

Lars Schmidt-Thieme, Ruth Janning, Carlotta Schatten, 2015, EDM.

Learning to Extract Relations for Relational Classification

Lars Schmidt-Thieme, Steffen Rendle, Christine Preisach, 2009, PAKDD.

Graph-Based Model-Selection Framework for Large Ensembles

Lars Schmidt-Thieme, Krisztian Buza, Alexandros Nanopoulos, 2010, HAIS.

Exploiting Semantic Product Descriptions for Recommender Systems

Lars Schmidt-Thieme, Georg Lausen, Cai-Nicolas Ziegler, 2004 .

Information Integration of Partially Labeled Data

Lars Schmidt-Thieme, Steffen Rendle, 2007, GfKl.

Invariant Time-Series Classification

Lars Schmidt-Thieme, Alexandros Nanopoulos, Josif Grabocka, 2012, ECML/PKDD.

Fast context-aware recommendations with factorization machines

Lars Schmidt-Thieme, Zeno Gantner, Steffen Rendle, 2011, SIGIR.

Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, October 23-25, 2009

Lars Schmidt-Thieme, Robin Burke, Alexander Felfernig, 2009, RecSys.

Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization

Lars Schmidt-Thieme, Nicolas Schilling, Martin Wistuba, 2016, ECML/PKDD.

Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems

Lars Schmidt-Thieme, Leandro Balby Marinho, Christine Preisach, 2010, PAKDD.

Deep Metric Learning for Ground Images pdf

Lars Schmidt-Thieme, Randolf Scholz, Jan Fabian Schmid, 2021, ArXiv.

RP-DQN: An application of Q-Learning to Vehicle Routing Problems pdf

Lars Schmidt-Thieme, Jonas K. Falkner, Ahmad Bdeir, 2021, KI.

Multimodal Meta-Learning for Time Series Regression

Lars Schmidt-Thieme, Sebastian Pineda-Arango, Felix Heinrich, 2021, AALTD@ECML/PKDD.

Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning

Lars Schmidt-Thieme, Jonas Sonntag, Michael Engel, 2021, AAAI.

Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2021, ECML/PKDD.

Hyperparameter Optimization with Differentiable Metafeatures pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2021, ArXiv.

Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling

Lars Schmidt-Thieme, Daniel Pototzky, Matthias Kirschner, 2021, GCPR.

Self-supervised Learning for Object Detection in Autonomous Driving

Lars Schmidt-Thieme, Daniel Pototzky, Matthias Kirschner, 2021, GCPR.

A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2021, SIGIR.

Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size pdf

Lars Schmidt-Thieme, Daniela Thyssens, Jonas Falkner, 2022, ArXiv.

Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting pdf

Lars Schmidt-Thieme, Nghia Duong-Trung, Stefan Born, 2021, ECML/PKDD.

Improving Hyperparameter Optimization by Planning Ahead pdf

Lars Schmidt-Thieme, Hadi S. Jomaa, Jonas Falkner, 2021, ArXiv.

Do We Really Need Deep Learning Models for Time Series Forecasting? pdf

Lars Schmidt-Thieme, Hadi Samer Jomaa, Daniela Thyssens, 2021, ArXiv.

Multi-script Handwritten Digit Recognition Using Multi-task Learning pdf

Lars Schmidt-Thieme, Durga Prasad Sharma, Randolf Scholz, 2021, ArXiv.

Context-Aware Factorization for Personalized Student ' s Task Recommendation

Lars Schmidt-Thieme, Nguyen Thai-Nghe, Tomas Horvath, 2011 .

Factorization Machines Factorized Polynomial Regression Models

Lars Schmidt-Thieme, Steffen Rendle, Christoph Freudenthaler, 2011 .

Relational Classification Using Automatically Extracted Relations by Record Linkage

Lars Schmidt-Thieme, Steffen Rendle, Christine Preisach, 2008 .

RECOMMENDER SYSTEMS BASED ON USER NAVIGATIONAL BEHAVIOR IN THE INTERNET

Wolfgang Gaul, Lars Schmidt-Thieme, W. Gaul, 2002 .

Building a Commodity Location-based Service at Botanic Garden of University of

Lars Schmidt-Thieme, L. Schmidt-Thieme, Ludmila Salaur, 2005 .

Eigenmode Identification in Campbell Diagrams

Lars Schmidt-Thieme, Krisztian Buza, Christine Preisach, 2009 .

Value-transformation for monotone prediction by approximating fuzzy membership functions

Lars Schmidt-Thieme, Krisztian Buza, Alan Eckhardt, 2011, 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI).