Schmidt-Thieme, Lars

31 publications

ICLR 2026 Reliable Probabilistic Forecasting of Irregular Time Series Through Marginalization-Consistent Flows Vijaya Krishna Yalavarthi, Randolf Scholz, Christian Klötergens, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme

ECML-PKDD 2025 Attribute and Context-Aware Multi-Behavior Model for Unique-Item Recommendation Shereen Elsayed, Ngoc Son Le, Ahmed Rashed, Lars Schmidt-Thieme

ECML-PKDD 2025 Attribute-Aware Sequential Recommendation Model for Used Car Auctions Shereen Elsayed, Ngoc Son Le, Ahmed Rashed, Lukas Hestermeyer, Radoslaw Wlodarczyk, Maximilian Stubbemann, Lars Schmidt-Thieme

AAAI 2025 Motif-Aware Graph Neural Networks for Networked Time Series Imputation Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

ICLR 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

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

NeurIPS 2025 Robust Hyperbolic Learning with Curvature-Aware Optimization Ahmad Bdeir, Johannes Burchert, Lars Schmidt-Thieme, Niels Landwehr

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

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

AAAI 2024 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

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

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

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

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

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

ICML 2022 Zero-Shot AutoML with Pretrained Models Ekrem Öztürk, Fabio Ferreira, Hadi Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter

ECML-PKDD 2021 Multi-Task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets Shayan Jawed, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka

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

NeurIPSW 2021 Transfer Learning for Bayesian HPO with End-to-End Landmark Meta-Features Hadi Samer Jomaa, Sebastian Pineda Arango, Lars Schmidt-Thieme, Josif Grabocka

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

ECML-PKDD 2019 A Deep Multi-Task Approach for Residual Value Forecasting Ahmed Rashed, Shayan Jawed, Jens Rehberg, Josif Grabocka, Lars Schmidt-Thieme, Andre Hintsches

MLJ 2018 Scalable Gaussian Process-Based Transfer Surrogates for Hyperparameter Optimization Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

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

ECML-PKDD 2016 Scalable Hyperparameter Optimization with Products of Gaussian Process Experts Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme

ECML-PKDD 2016 Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

ECML-PKDD 2015 Hyperparameter Optimization with Factorized Multilayer Perceptrons Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme

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

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

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

ECML-PKDD 2012 Invariant Time-Series Classification Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme

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