| 2026 | PAKDD | Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased Is Time Series Forecasting? | Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Christian Kltergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme |
| 2026 | PAKDD | HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting. | Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme |
| 2026 | PAKDD | Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting. | Christian Kltergens, Tim Dernedde, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi |
| 2026 | PAKDD | An Empirical Analysis of Distributional Effects in Learning-Based Routing Systems. | Daniela Thyssens, Tim Dernedde, Lars Schmidt-Thieme |
| 2025 | AAAI | Motif-aware Graph Neural Networks for Networked Time Series Imputation. | Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme |
| 2025 | AAAI | Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. | Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme |
| 2025 | ECAI | TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression. | Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme |
| 2025 | ICLR | Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. | Christian Kltergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme |
| 2025 | PAKDD | Moco: A Learnable Meta Optimizer for Combinatorial Optimization. | Tim Dernedde, Daniela Thyssens, Sren Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme |
| 2024 | AAAI | 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 |
| 2024 | PAKDD | HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation. | Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme |
| 2024 | PAKDD | Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting. | Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme |
| 2024 | RecSys | Multi-Behavioral Sequential Recommendation. | Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme |
| 2023 | DSAA | Sparse Self-Attention Guided Generative Adversarial Networks for Time-Series Generation. | Nourhan Ahmed, Lars Schmidt-Thieme |
| 2023 | IJCAI | Neural Capacitated Clustering. | Jonas K. Falkner, Lars Schmidt-Thieme |
| 2023 | IJCNN | Deep Multi-Representation Model for Click-Through Rate Prediction. | Shereen Elsayed, Lars Schmidt-Thieme |
| 2023 | IJCNN | Forecasting Early with Meta Learning. | Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme |
| 2023 | PAKDD | Few-Shot Human Motion Prediction for Heterogeneous Sensors. | Rafael Rgo Drumond, Lukas Brinkmeyer, Lars Schmidt-Thieme |
| 2022 | DSAA | Positive-Unlabeled Domain Adaptation. | Jonas Sonntag, Gunnar Behrens, Lars Schmidt-Thieme |
| 2022 | DSAA | DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. | Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme |
| 2022 | ICML | Zero-shot AutoML with Pretrained Models. | Ekrem ztrk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter |
| 2022 | KI | Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning. | Christian Lwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon, Jonas K. Falkner, Lars Schmidt-Thieme |
| 2022 | PAKDD | Open Set Recognition for Time Series Classification. | Tolga Akar, Thorben Werner, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme |
| 2022 | RecSys | Context and Attribute-Aware Sequential Recommendation via Cross-Attention. | Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme |
| 2021 | AAAI | Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning. | Jonas Sonntag, Michael Engel, Lars Schmidt-Thieme |
| 2021 | BMVC | Training Object Detectors if Only Large Objects are Labeled. | Daniel Pototzky, Matthias Kirschner, Azhar Sultan, Lars Schmidt-Thieme |
| 2021 | KI | 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 |
| 2021 | SIGIR | A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme |
| 2020 | GECCO | An efficient evolutionary solution to the joint order batching - order picking planning problem. | Riccardo Lucato, Jonas K. Falkner, Lars Schmidt-Thieme |
| 2020 | PAKDD | Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD. | Mohsan Jameel, Shayan Jawed, Lars Schmidt-Thieme |
| 2020 | PAKDD | Self-supervised Learning for Semi-supervised Time Series Classification. | Shayan Jawed, Josif Grabocka, Lars Schmidt-Thieme |
| 2020 | RecSys | MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. | Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches |
| 2020 | SDM | HIDRA: Head Initialization across Dynamic targets for Robust Architectures. | Rafael Rgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme |
| 2019 | ICAART | Multi-Label Network Classification via Weighted Personalized Factorizations. | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme |
| 2019 | ICAART | Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme |
| 2019 | IJCNN | A Hybrid Convolutional Approach for Parking Availability Prediction. | Hadi Samer Jomaa, Josif Grabocka, Lars Schmidt-Thieme, Alexander Borek |
| 2019 | KDD | Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings. | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme |
| 2019 | RecSys | Attribute-aware non-linear co-embeddings of graph features. | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme |
| 2018 | SIGIR | Towards Distributed Pairwise Ranking using Implicit Feedback. | Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme |
| 2017 | CIKM | On Discovering the Number of Document Topics via Conceptual Latent Space. | Nghia Duong-Trung, Lars Schmidt-Thieme |
| 2017 | PAKDD | Personalized Deep Learning for Tag Recommendation. | Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rgo Drumond, Lars Schmidt-Thieme |
| 2017 | SDM | Automatic Frankensteining: Creating Complex Ensembles Autonomously. | Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme |
| 2016 | CIKM | Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression. | Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme |
| 2016 | COLING | Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF. | Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme |
| 2016 | CSEDU | Student Progress Modeling with Skills Deficiency Aware Kalman Filters. | Carlotta Schatten, Lars Schmidt-Thieme |
| 2016 | CSEDU | Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems. | Carlotta Schatten, Lars Schmidt-Thieme |
| 2016 | DSAA | Hyperparameter Optimization Machines. | Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme |
| 2016 | LREC | Learning Thesaurus Relations from Distributional Features. | Rosa Tsegaye Aga, Christian Wartena, Lucas Drumond, Lars Schmidt-Thieme |
| 2015 | AAAI | Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS. | Carlotta Schatten, Ruth Janning, Lars Schmidt-Thieme |
| 2015 | AIED | Recognizing Perceived Task Difficulty from Speech, Pause Histograms. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2015 | DSAA | Learning hyperparameter optimization initializations. | Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme |
| 2015 | EDM | Recognizing Perceived Task Difficulty from Speech and Pause Histograms. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2015 | EDM | How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2015 | EDM | A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets. | Lydia Vo, Carlotta Schatten, Claudia Mazziotti, Lars Schmidt-Thieme |
| 2015 | ICDM | Sequential Model-Free Hyperparameter Tuning. | Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme |
| 2015 | ICTAI | Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. | Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme |
| 2014 | CIKM | Optimizing Multi-Relational Factorization Models for Multiple Target Relations. | Lucas Rgo Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Wolfgang Nejdl |
| 2014 | CSEDU | Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach. | Carlotta Schatten, Ruth Janning, Lars Schmidt-Thieme |
| 2014 | CSEDU | Adaptive Content Sequencing without Domain Information. | Carlotta Schatten, Lars Schmidt-Thieme |
| 2014 | EDM | Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2014 | EDM | Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems. | Carlotta Schatten, Ruth Janning, Manolis Mavrikis, Lars Schmidt-Thieme |
| 2014 | ICALT | Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. | Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutirrez Santos |
| 2014 | ICTAI | An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme, Gerhard Backfried, Norbert Pfannerer |
| 2014 | ISMIS | Automatic Subclasses Estimation for a Better Classification with HNNP. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2014 | KDD | Learning time-series shapelets. | Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme |
| 2014 | KI | Local Feature Extractors Accelerating HNNP for Phoneme Recognition. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2014 | PAKDD | Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification. | Lucas Rgo Drumond, Lars Schmidt-Thieme, Christoph Freudenthaler, Artus Krohn-Grimberghe |
| 2014 | PAKDD | Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. | Josif Grabocka, Erind Bedalli, Lars Schmidt-Thieme |
| 2013 | DaWaK | Supervised Dimensionality Reduction via Nonlinear Target Estimation. | Josif Grabocka, Lucas Drumond, Lars Schmidt-Thieme |
| 2013 | ICTAI | HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information. | Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme |
| 2013 | ICTAI | Factorized Decision Trees for Active Learning in Recommender Systems. | Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2013 | KI | Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. | Martin Wistuba, Lars Schmidt-Thieme |
| 2013 | WWW | Towards real-time collaborative filtering for big fast data. | Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme |
| 2012 | AAAI | Classification of Sparse Time Series via Supervised Matrix Factorization. | Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2012 | CIKM | 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 |
| 2012 | RecSys | Real-time top-n recommendation in social streams. | Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl |
| 2012 | RecSys | Exploiting the characteristics of matrix factorization for active learning in recommender systems. | Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2012 | SAC | Predicting RDF triples in incomplete knowledge bases with tensor factorization. | Lucas Drumond, Steffen Rendle, Lars Schmidt-Thieme |
| 2012 | WSDM | Multi-relational matrix factorization using bayesian personalized ranking for social network data. | Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2011 | CIDM | IQ estimation for accurate time-series classification. | Krisztin Bza, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | CIDM | Active learning for aspect model in recommender systems. | Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | CSEDU | Matrix and Tensor Factorization for Predicting Student Performance. | Nguyen Thai-Nghe, Lucas Drumond, Toms Horvth, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | EDM | Factorization Models for Forecasting Student Performance. | Nguyen Thai-Nghe, Toms Horvth, Lars Schmidt-Thieme |
| 2011 | HAIS | Fusion of Similarity Measures for Time Series Classification. | Krisztin Bza, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | ICALT | Personalized Forecasting Student Performance. | Nguyen Thai-Nghe, Toms Horvth, Lars Schmidt-Thieme |
| 2011 | IJCAI | Keyword-Based TV Program Recommendation. | Christian Wartena, Wout Slakhorst, Martin Wibbels, Zeno Gantner, Christoph Freudenthaler, Chris Newell, Lars Schmidt-Thieme |
| 2011 | IJCNN | A new evaluation measure for learning from imbalanced data. | Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme |
| 2011 | IRI | Non-myopic active learning for recommender systems based on Matrix Factorization. | Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | ICTAI | Towards Optimal Active Learning for Matrix Factorization in Recommender Systems. | Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | ICWSM | Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. | Timo Reuter, Philipp Cimiano, Lucas Drumond, Krisztin Bza, Lars Schmidt-Thieme |
| 2011 | PAKDD | INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. | Krisztin Bza, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2011 | RecSys | MyMediaLite: a free recommender system library. | Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2011 | SIGIR | Fast context-aware recommendations with factorization machines. | Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2010 | DOLAP | Integrating OLAP and recommender systems: an evaluation perspective. | Artus Krohn-Grimberghe, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2010 | HAIS | Graph-Based Model-Selection Framework for Large Ensembles. | Krisztin Bza, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2010 | ICDM | Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. | Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme |
| 2010 | IJCNN | Cost-sensitive learning methods for imbalanced data. | Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme |
| 2010 | PAKDD | Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems. | Christine Preisach, Leandro Balby Marinho, Lars Schmidt-Thieme |
| 2010 | RecSys | Workshop on user-centric evaluation of recommender systems and their interfaces. | Bart P. Knijnenburg, Lars Schmidt-Thieme, Dirk G. F. M. Bollen |
| 2010 | WWW | Factorizing personalized Markov chains for next-basket recommendation. | Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2010 | WSDM | Pairwise interaction tensor factorization for personalized tag recommendation. | Steffen Rendle, Lars Schmidt-Thieme |
| 2009 | GECCO | Swarming to rank for information retrieval. | Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-Thieme |
| 2009 | ISDA | Improving Academic Performance Prediction by Dealing with Class Imbalance. | Nguyen Thai-Nghe, Andr Busche, Lars Schmidt-Thieme |
| 2009 | KDD | Learning optimal ranking with tensor factorization for tag recommendation. | Steffen Rendle, Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2009 | PAKDD | Learning to Extract Relations for Relational Classification. | Steffen Rendle, Christine Preisach, Lars Schmidt-Thieme |
| 2009 | UAI | BPR: Bayesian Personalized Ranking from Implicit Feedback. | Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme |
| 2008 | ICDM | Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference. | Steffen Rendle, Lars Schmidt-Thieme |
| 2008 | PAKDD | Scaling Record Linkage to Non-uniform Distributed Class Sizes. | Steffen Rendle, Lars Schmidt-Thieme |
| 2008 | RecSys | Online-updating regularized kernel matrix factorization models for large-scale recommender systems. | Steffen Rendle, Lars Schmidt-Thieme |
| 2008 | SAC | Tag-aware recommender systems by fusion of collaborative filtering algorithms. | Karen H. L. Tso-Sutter, Leandro Balby Marinho, Lars Schmidt-Thieme |
| 2007 | ICMLA | Combining multi-distributed mixture models and bayesian networks for semi-supervised learning. | Manuel Stritt, Lars Schmidt-Thieme, Gerhard Poeppel |
| 2006 | ICDM | Relational Ensemble Classification. | Christine Preisach, Lars Schmidt-Thieme |
| 2006 | ICDM | Object Identification with Constraints. | Steffen Rendle, Lars Schmidt-Thieme |
| 2006 | PAKDD | Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics. | Karen H. L. Tso, Lars Schmidt-Thieme |
| 2005 | GI | Fachinformationssystem Informatik (FIS-I) und Semantische Technologien fr Informationsportale (SemIPort). | Peter Fankhauser, Norbert Fuhr, Jens Hartmann, Anthony Jameson, Claus-Peter Klas, Stefan Klink, Agnes Koschmider, Sascha Kriewel, Patrick Lehti, Peter Luksch, Ernst W. Mayr, Andreas Oberweis, Paul Ortyl, Stefan Pfingstl, Patrick Reuther, Ute Rusnak, Guido Sautter, Klemens Bhm, Andr Schaefer, Lars Schmidt-Thieme, Eric Schwarzkopf, Nenad Stojanovic, Rudi Studer, Roland Vollmar, Bernd Walter, Alexander Weber |
| 2005 | ICDM | Compound Classification Models for Recommender Systems. | Lars Schmidt-Thieme |
| 2004 | CIKM | Taxonomy-driven computation of product recommendations. | Cai-Nicolas Ziegler, Georg Lausen, Lars Schmidt-Thieme |
| 2001 | ICDM | Mining Generalized Association Rules for Sequential and Path Data. | Wolfgang Gaul, Lars Schmidt-Thieme |