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ORCIDLars Schmidt-Thieme
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- affiliation: University of Hildesheim, Institute of Computer Science, Germany
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2020 – today
- 2025
[c204]Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Motif-aware Graph Neural Networks for Networked Time Series Imputation. AAAI 2025: 11409-11417- [c203]Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme:
Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. AAAI 2025: 21877-21885 - [c202]Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme:
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. ICLR 2025 - [i72]Thorben Werner, Lars Schmidt-Thieme:
Bayesian Active Learning By Distribution Disagreement. CoRR abs/2501.01248 (2025) - [i71]Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme:
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs. CoRR abs/2502.07489 (2025) - [i70]Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting? CoRR abs/2502.09683 (2025) - [i69]Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme:
IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting. CoRR abs/2502.11816 (2025) - [i68]Diego Coello de Portugal Mecke, Haya Alyoussef, Ilia Koloiarov, Maximilian Stubbemann, Lars Schmidt-Thieme:
STADE: Standard Deviation as a Pruning Metric. CoRR abs/2503.22451 (2025) - 2024
- [j22]Nourhan Ahmed
, Lars Schmidt-Thieme:
Structure-aware decoupled imputation network for multivariate time series. Data Min. Knowl. Discov. 38(3): 1006-1026 (2024) - [j21]Nourhan Ahmed, Ahmed Rashed, Lars Schmidt-Thieme:
Learning attentive attribute-aware node embeddings in dynamic environments. Int. J. Data Sci. Anal. 17(2): 189-201 (2024) - [c201]Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme:
GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. AAAI 2024: 16255-16263 - [c200]Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme:
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing. IEEE Big Data 2024: 2179-2187 - [c199]Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme:
A Cross-Domain Benchmark for Active Learning. NeurIPS 2024 - [c198]Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme:
HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation. PAKDD (5) 2024: 131-143 - [c197]Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme:
Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting. PAKDD (6) 2024: 264-275 - [c196]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. ECML/PKDD (4) 2024: 421-436 - [c195]Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme:
Multi-Behavioral Sequential Recommendation. RecSys 2024: 902-906 - [c194]Indra Firmansyah, Randolf Scholz, Adrian Nahmendorff, Ngoc Son Le, Shereen Elsayed, Lars Schmidt-Thieme:
Learning Set Embeddings for Fashion Compatibility Recommendation. SURE@RecSys 2024 - [i67]Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme:
Moco: A Learnable Meta Optimizer for Combinatorial Optimization. CoRR abs/2402.04915 (2024) - [i66]Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme:
Probabilistic Forecasting of Irregular Time Series via Conditional Flows. CoRR abs/2402.06293 (2024) - [i65]Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme:
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing. CoRR abs/2403.03812 (2024) - [i64]Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme:
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting. CoRR abs/2403.04477 (2024) - [i63]Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi, Diego Coello de Portugal, Maximilian Stubbemann, Lars Schmidt-Thieme:
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. CoRR abs/2404.06966 (2024) - [i62]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. CoRR abs/2405.03582 (2024) - [i61]Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme:
HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation. CoRR abs/2405.09638 (2024) - [i60]Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme:
Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. CoRR abs/2406.07246 (2024) - [i59]Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme:
A Cross-Domain Benchmark for Active Learning. CoRR abs/2408.00426 (2024) - 2023
- [j20]Nourhan Ahmed, Lars Schmidt-Thieme:
Sparse self-attention guided generative adversarial networks for time-series generation. Int. J. Data Sci. Anal. 16(4): 421-434 (2023) - [c193]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series. IEEE Big Data 2023: 986-995 - [c192]Shayan Jawed, Jan Stening, Lars Schmidt-Thieme:
Pricing Used Vehicles at Volkswagen Financial Services AG. IEEE Big Data 2023: 1736-1743 - [c191]Nourhan Ahmed, Lars Schmidt-Thieme:
Sparse Self-Attention Guided Generative Adversarial Networks for Time-Series Generation. DSAA 2023: 1-2 - [c190]Jonas K. Falkner, Lars Schmidt-Thieme:
Neural Capacitated Clustering. IJCAI 2023: 3686-3694 - [c189]Shereen Elsayed, Lars Schmidt-Thieme:
Deep Multi-Representation Model for Click-Through Rate Prediction. IJCNN 2023: 1-9 - [c188]Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Forecasting Early with Meta Learning. IJCNN 2023: 1-8 - [c187]Christian Löwens, Daniela Thyssens, Emma Andersson, Christina Jenkins, Lars Schmidt-Thieme:
DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision. ITSC 2023: 3742-3748 - [c186]Rafael Rêgo Drumond, Lukas Brinkmeyer, Lars Schmidt-Thieme:
Few-Shot Human Motion Prediction for Heterogeneous Sensors. PAKDD (2) 2023: 551-563 - [c185]Mofassir ul Islam Arif, Johannes Burchert, Lars Schmidt-Thieme:
ConvMix: Combining Intermediate Latent Features in Deep Convolutional Neural Networks. SCIA (2) 2023: 156-174 - [i58]Jonas K. Falkner, Lars Schmidt-Thieme:
Neural Capacitated Clustering. CoRR abs/2302.05134 (2023) - [i57]Mofassir ul Islam Arif, Mohsan Jameel, Lars Schmidt-Thieme:
Directly Optimizing IoU for Bounding Box Localization. CoRR abs/2304.07256 (2023) - [i56]Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka, Lars Schmidt-Thieme:
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks. CoRR abs/2304.07262 (2023) - [i55]Vijaya Krishna Yalavarthi, Kiran Madusudanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme:
Forecasting Irregularly Sampled Time Series using Graphs. CoRR abs/2305.12932 (2023) - [i54]Christian Löwens, Daniela Thyssens, Emma Andersson, Christina Jenkins, Lars Schmidt-Thieme:
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision. CoRR abs/2306.06068 (2023) - [i53]Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Forecasting Early with Meta Learning. CoRR abs/2307.09796 (2023) - [i52]Jonas K. Falkner, Lars Schmidt-Thieme:
Too Big, so Fail? - Enabling Neural Construction Methods to Solve Large-Scale Routing Problems. CoRR abs/2309.17089 (2023) - [i51]Daniela Thyssens, Tim Dernedde, Jonas K. Falkner, Lars Schmidt-Thieme:
Routing Arena: A Benchmark Suite for Neural Routing Solvers. CoRR abs/2310.04140 (2023) - [i50]Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme:
Context-Aware Sequential Model for Multi-Behaviour Recommendation. CoRR abs/2312.09684 (2023) - 2022
- [c184]Shayan Jawed, Lars Schmidt-Thieme:
GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting. IEEE Big Data 2022: 992-1001 - [c183]Jonas Schnepf, Paula Vetter, Tarik Temel, Bernd Scheuermann, Lars Schmidt-Thieme:
On the Potential of Using ERP Business and System Data for Fraud Detection. IEEE Big Data 2022: 3081-3091 - [c182]Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme:
FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU. GCPR 2022: 53-67 - [c181]Jonas Sonntag, Gunnar Behrens, Lars Schmidt-Thieme:
Positive-Unlabeled Domain Adaptation. DSAA 2022: 1-10 - [c180]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. DSAA 2022: 1-10 - [c179]Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter:
Zero-shot AutoML with Pretrained Models. ICML 2022: 17138-17155 - [c178]Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme:
Parting With Illusions About Synthetic Data. IVMSP 2022: 1-4 - [c177]Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme:
Does Self-Supervised Pretraining Really Match ImageNet Weights? IVMSP 2022: 1-5 - [c176]Christian Löwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon, Jonas K. Falkner, Lars Schmidt-Thieme:
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning. KI 2022: 160-172 - [c175]Marta Kipke, Lukas Brinkmeyer, Souaybou Bagayoko, Lars Schmidt-Thieme, Martin Langner:
Deep Level Annotation for Painter Attribution on Greek Vases utilizing Object Detection. SUMAC @ ACM Multimedia 2022: 23-31 - [c174]Tolga Akar, Thorben Werner, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Open Set Recognition for Time Series Classification. PAKDD (2) 2022: 354-366 - [c173]Mesay Samuel Samuel, Lars Schmidt-Thieme, Durga Prasad Sharma, Abiot Sinamo Boltena, Abey Bruck:
Offline Handwritten Amharic Character Recognition Using Few-Shot Learning. PanAfriCon AI 2022: 233-244 - [c172]Lukas Brinkmeyer, Rafael Rêgo Drumond, Johannes Burchert, Lars Schmidt-Thieme:
Few-Shot Forecasting of Time-Series with Heterogeneous Channels. ECML/PKDD (6) 2022: 3-18 - [c171]Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme:
U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. ECML/PKDD (6) 2022: 36-52 - [c170]Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, Lars Schmidt-Thieme:
Learning to Control Local Search for Combinatorial Optimization. ECML/PKDD (5) 2022: 361-376 - [c169]Ahmad Bdeir, Jonas K. Falkner, Lars Schmidt-Thieme:
Attention, Filling in the Gaps for Generalization in Routing Problems. ECML/PKDD (6) 2022: 505-520 - [c168]Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme:
Context and Attribute-Aware Sequential Recommendation via Cross-Attention. RecSys 2022: 71-80 - [i49]Daniela Thyssens, Jonas K. Falkner, Lars Schmidt-Thieme:
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size. CoRR abs/2201.01529 (2022) - [i48]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:
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG. CoRR abs/2202.04411 (2022) - [i47]Jonas Sonntag, Gunnar Behrens, Lars Schmidt-Thieme:
Positive-Unlabeled Domain Adaptation. CoRR abs/2202.05695 (2022) - [i46]Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, Abiot Sinamo Boltena:
Improving Amharic Handwritten Word Recognition Using Auxiliary Task. CoRR abs/2202.12687 (2022) - [i45]Lukas Brinkmeyer, Rafael Rêgo Drumond, Johannes Burchert, Lars Schmidt-Thieme:
Few-Shot Forecasting of Time-Series with Heterogeneous Channels. CoRR abs/2204.03456 (2022) - [i44]Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme:
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention. CoRR abs/2204.06519 (2022) - [i43]Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme:
Large Neighborhood Search based on Neural Construction Heuristics. CoRR abs/2205.00772 (2022) - [i42]Shereen Elsayed, Lukas Brinkmeyer, Lars Schmidt-Thieme:
End-to-End Image-Based Fashion Recommendation. CoRR abs/2205.02923 (2022) - [i41]Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter:
Zero-Shot AutoML with Pretrained Models. CoRR abs/2206.08476 (2022) - [i40]Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, Lars Schmidt-Thieme:
Learning to Control Local Search for Combinatorial Optimization. CoRR abs/2206.13181 (2022) - [i39]Christian Löwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon, Jonas K. Falkner, Lars Schmidt-Thieme:
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning. CoRR abs/2207.01443 (2022) - [i38]Ahmad Bdeir, Jonas K. Falkner, Lars Schmidt-Thieme:
Attention, Filling in The Gaps for Generalization in Routing Problems. CoRR abs/2207.07212 (2022) - [i37]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. CoRR abs/2208.11374 (2022) - [i36]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:
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. CoRR abs/2209.01083 (2022) - [i35]Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, Abiot Sinamo Boltena, Abey Bruck:
Offline Handwritten Amharic Character Recognition Using Few-shot Learning. CoRR abs/2210.00275 (2022) - [i34]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
Tripletformer for Probabilistic Interpolation of Asynchronous Time Series. CoRR abs/2210.02091 (2022) - [i33]Shereen Elsayed, Lars Schmidt-Thieme:
Deep Multi-Representation Model for Click-Through Rate Prediction. CoRR abs/2210.10664 (2022) - [i32]Shayan Jawed, Lars Schmidt-Thieme:
Auxiliary Quantile Forecasting with Linear Networks. CoRR abs/2212.02578 (2022) - [i31]Nghia Duong-Trung, Stefan Born, Kiran Madhusudhanan, Randolf Scholz, Johannes Burchert, Danh Le Phuoc, Lars Schmidt-Thieme:
Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series. CoRR abs/2212.07771 (2022) - [i30]Rafael Rêgo Drumond, Lukas Brinkmeyer, Lars Schmidt-Thieme:
Few-shot human motion prediction for heterogeneous sensors. CoRR abs/2212.11771 (2022) - 2021
- [j19]Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka:
Dataset2Vec: learning dataset meta-features. Data Min. Knowl. Discov. 35(3): 964-985 (2021) - [c167]Jonas Sonntag, Michael Engel, Lars Schmidt-Thieme:
Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning. AAAI 2021: 15408-15415 - [c166]Sebastian Pineda-Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme:
Multimodal Meta-Learning for Time Series Regression. AALTD@ECML/PKDD 2021: 123-138 - [c165]Daniel Pototzky, Matthias Kirschner, Azhar Sultan, Lars Schmidt-Thieme:
Training Object Detectors if Only Large Objects are Labeled. BMVC 2021: 68 - [c164]Daniel Pototzky, Matthias Kirschner, Lars Schmidt-Thieme:
Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling. GCPR 2021: 439-452 - [c163]Daniel Pototzky, Azhar Sultan, Matthias Kirschner, Lars Schmidt-Thieme:
Self-supervised Learning for Object Detection in Autonomous Driving. GCPR 2021: 484-497 - [c162]Ahmad Bdeir, Simon Boeder, Tim Dernedde, Kirill Tkachuk, Jonas K. Falkner, Lars Schmidt-Thieme:
RP-DQN: An Application of Q-Learning to Vehicle Routing Problems. KI 2021: 3-16 - [c161]Shayan Jawed, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka:
Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets. ECML/PKDD (1) 2021: 485-501 - [c160]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. SIGIR 2021: 605-613 - [i29]Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, Hadi Samer Jomaa:
Do We Really Need Deep Learning Models for Time Series Forecasting? CoRR abs/2101.02118 (2021) - [i28]Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka:
Hyperparameter Optimization with Differentiable Metafeatures. CoRR abs/2102.03776 (2021) - [i27]Ahmad Bdeir, Simon Boeder, Tim Dernedde, Kirill Tkachuk, Jonas K. Falkner, Lars Schmidt-Thieme:
RP-DQN: An application of Q-Learning to Vehicle Routing Problems. CoRR abs/2104.12226 (2021) - [i26]Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, Randolf Scholz:
Multi-script Handwritten Digit Recognition Using Multi-task Learning. CoRR abs/2106.08267 (2021) - [i25]Sebastian Pineda-Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme:
Multimodal Meta-Learning for Time Series Regression. CoRR abs/2108.02842 (2021) - [i24]Raaghav Radhakrishnan, Jan Fabian Schmid, Randolf Scholz, Lars Schmidt-Thieme:
Deep Metric Learning for Ground Images. CoRR abs/2109.01569 (2021) - [i23]Hadi S. Jomaa, Jonas K. Falkner, Lars Schmidt-Thieme:
Improving Hyperparameter Optimization by Planning Ahead. CoRR abs/2110.08028 (2021) - [i22]Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme:
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. CoRR abs/2110.08255 (2021) - 2020
- [c159]Riccardo Lucato, Jonas K. Falkner, Lars Schmidt-Thieme:
An efficient evolutionary solution to the joint order batching - order picking planning problem. GECCO Companion 2020: 103-104 - [c158]Jonas Sonntag, Lars Schmidt-Thieme:
A machine learning approach to infer on-street parking occupancy based on parking meter transactions. ITSC 2020: 1-6 - [c157]Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka, Lars Schmidt-Thieme:
Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks. LWDA 2020: 47-58 - [c156]Shayan Jawed, Josif Grabocka, Lars Schmidt-Thieme:
Self-supervised Learning for Semi-supervised Time Series Classification. PAKDD (1) 2020: 499-511 - [c155]Mohsan Jameel, Shayan Jawed, Lars Schmidt-Thieme:
Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD. PAKDD (2) 2020: 894-905 - [c154]Mohsan Jameel, Mofassir ul Islam Arif, Andre Hintsches, Lars Schmidt-Thieme:
Automation of Leasing Vehicle Return Assessment Using Deep Learning Models. ECML/PKDD (4) 2020: 259-274 - [c153]Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches:
MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. RecSys 2020: 230-239 - [c152]Rafael Rêgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme:
HIDRA: Head Initialization across Dynamic targets for Robust Architectures. SDM 2020: 397-405 - [i21]Jonas K. Falkner, Lars Schmidt-Thieme:
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention. CoRR abs/2006.09100 (2020)
2010 – 2019
- 2019
- [c151]Mofassir ul Islam Arif, Mohsan Jameel, Lars Schmidt-Thieme:
Directly Optimizing IoU for Bounding Box Localization. ACPR (1) 2019: 544-556 - [c150]Shayan Jawed, Ahmed Rashed, Lars Schmidt-Thieme:
Multi-step Forecasting via Multi-task Learning. IEEE BigData 2019: 790-799 - [c149]Vijaya Krishna Yalavarthi, Josif Grabocka, Hareesh Mandalapu, Lars Schmidt-Thieme:
Gait Verification using Deep Learning with a Pairwise Loss. BIOSIG 2019: 141-152 - [c148]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. ICAART (Revised Selected Papers) 2019: 100-117 - [c147]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Multi-Label Network Classification via Weighted Personalized Factorizations. ICAART (2) 2019: 357-366 - [c146]Hadi Samer Jomaa, Josif Grabocka, Lars Schmidt-Thieme, Alexander Borek:
A Hybrid Convolutional Approach for Parking Availability Prediction. IJCNN 2019: 1-8 - [c145]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings. KDD 2019: 1132-1140 - [c144]Mohsan Jameel, Josif Grabocka, Mofassir ul Islam Arif, Lars Schmidt-Thieme:
Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD. PKDD/ECML Workshops (1) 2019: 333-341 - [c143]Ahmed Rashed, Shayan Jawed, Jens Rehberg, Josif Grabocka, Lars Schmidt-Thieme, Andre Hintsches:
A Deep Multi-task Approach for Residual Value Forecasting. ECML/PKDD (3) 2019: 467-482 - [c142]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Attribute-aware non-linear co-embeddings of graph features. RecSys 2019: 314-321 - [i20]Shayan Jawed, Eya Boumaiza, Josif Grabocka, Lars Schmidt-Thieme:
Data-Driven Vehicle Trajectory Forecasting. CoRR abs/1902.05400 (2019) - [i19]Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme:
Multi-Label Network Classification via Weighted Personalized Factorizations. CoRR abs/1902.09294 (2019) - [i18]Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme:
Learning Surrogate Losses. CoRR abs/1905.10108 (2019) - [i17]Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme:
Dataset2Vec: Learning Dataset Meta-Features. CoRR abs/1905.11063 (2019) - [i16]Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme:
In Hindsight: A Smooth Reward for Steady Exploration. CoRR abs/1906.09781 (2019) - [i15]Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme:
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning. CoRR abs/1906.11527 (2019) - [i14]Mesay Samuel Gondere, Lars Schmidt-Thieme, Abiot Sinamo Boltena, Hadi Samer Jomaa:
Handwritten Amharic Character Recognition Using a Convolutional Neural Network. CoRR abs/1909.12943 (2019) - [i13]Lukas Brinkmeyer, Rafael Rêgo Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme:
Chameleon: Learning Model Initializations Across Tasks With Different Schemas. CoRR abs/1909.13576 (2019) - [i12]Rafael Rêgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme:
HIDRA: Head Initialization across Dynamic targets for Robust Architectures. CoRR abs/1910.12749 (2019) - 2018
- [j18]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. Mach. Learn. 107(1): 43-78 (2018) - [c141]Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme:
Towards Distributed Pairwise Ranking using Implicit Feedback. SIGIR 2018: 973-976 - [i11]Josif Grabocka, Lars Schmidt-Thieme:
NeuralWarp: Time-Series Similarity with Warping Networks. CoRR abs/1812.08306 (2018) - 2017
- [j17]Muhammad Umer Khan, Lars Schmidt-Thieme, Alexandros Nanopoulos:
Collaborative SVM classification in scale-free peer-to-peer networks. Expert Syst. Appl. 69: 74-86 (2017) - [c140]Nghia Duong-Trung, Lars Schmidt-Thieme:
On Discovering the Number of Document Topics via Conceptual Latent Space. CIKM 2017: 2051-2054 - [c139]Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Finding Hierarchy of Topics from Twitter Data. LWDA 2017: 39 - [c138]Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme:
Personalized Deep Learning for Tag Recommendation. PAKDD (1) 2017: 186-197 - [c137]Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme:
Personalized Tag Recommendation for Images Using Deep Transfer Learning. ECML/PKDD (2) 2017: 705-720 - [c136]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Automatic Frankensteining: Creating Complex Ensembles Autonomously. SDM 2017: 741-749 - [i10]Dripta S. Raychaudhuri, Josif Grabocka, Lars Schmidt-Thieme:
Channel masking for multivariate time series shapelets. CoRR abs/1711.00812 (2017) - 2016
- [j16]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems. Int. J. Artif. Intell. Educ. 26(3): 855-876 (2016) - [j15]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. 49(2): 429-454 (2016) - [j14]Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme:
Latent Time-Series Motifs. ACM Trans. Knowl. Discov. Data 11(1): 6:1-6:20 (2016) - [c135]Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression. CIKM 2016: 1973-1976 - [c134]Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning DTW-Shapelets for Time-Series Classification. CODS 2016: 3:1-3:8 - [c133]Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme:
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF. COLING 2016: 2708-2717 - [c132]Carlotta Schatten, Lars Schmidt-Thieme:
Student Progress Modeling with Skills Deficiency Aware Kalman Filters. CSEDU (1) 2016: 31-42 - [c131]Carlotta Schatten, Lars Schmidt-Thieme:
Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems. CSEDU (Selected Papers) 2016: 49-70 - [c130]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Optimization Machines. DSAA 2016: 41-50 - [c129]Rosa Tsegaye Aga, Christian Wartena, Lucas Drumond, Lars Schmidt-Thieme:
Learning Thesaurus Relations from Distributional Features. LREC 2016 - [c128]Nghia Duong-Trung, Nicolas Schilling, Lucas Drumond, Lars Schmidt-Thieme:
Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream. LWDA 2016: 89-100 - [c127]Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. ECML/PKDD (1) 2016: 33-48 - [c126]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. ECML/PKDD (1) 2016: 199-214 - [i9]Lucas Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme:
Multi-Relational Learning at Scale with ADMM. CoRR abs/1604.00647 (2016) - [i8]Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Bank Card Usage Prediction Exploiting Geolocation Information. CoRR abs/1610.03996 (2016) - 2015
- [j13]Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme:
Beyond Manual Tuning of Hyperparameters. Künstliche Intell. 29(4): 329-337 (2015) - [j12]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. IEEE Trans. Knowl. Data Eng. 27(6): 1683-1695 (2015) - [j11]Josif Grabocka, Lars Schmidt-Thieme:
Learning Through Non-linearly Supervised Dimensionality Reduction. Trans. Large Scale Data Knowl. Centered Syst. 17: 74-96 (2015) - [j10]Rasoul Karimi, Alexandros Nanopoulos, Lars Schmidt-Thieme:
A supervised active learning framework for recommender systems based on decision trees. User Model. User Adapt. Interact. 25(1): 39-64 (2015) - [c125]Carlotta Schatten, Ruth Janning, Lars Schmidt-Thieme:
Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS. AAAI 2015: 1380-1386 - [c124]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Recognizing Perceived Task Difficulty from Speech, Pause Histograms. AIED Workshops 2015 - [c123]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning hyperparameter optimization initializations. DSAA 2015: 1-10 - [c122]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Improving Automatic Affect Recognition on Low-Level Speech Features in Intelligent Tutoring Systems. EC-TEL 2015: 169-182 - [c121]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Recognizing Perceived Task Difficulty from Speech and Pause Histograms. EDM (Workshops) 2015 - [c120]Lydia Voß, Carlotta Schatten, Claudia Mazziotti, Lars Schmidt-Thieme:
A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets. EDM 2015: 372-375 - [c119]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
How to Aggregate Multimodal Features for Perceived Task Difficulty Recognition in Intelligent Tutoring Systems. EDM 2015: 566-567 - [c118]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Sequential Model-Free Hyperparameter Tuning. ICDM 2015: 1033-1038 - [c117]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI 2015: 72-79 - [c116]Nguyen Thai-Nghe, Lars Schmidt-Thieme:
Multi-relational Factorization Models for Student Modeling in Intelligent Tutoring Systems. KSE 2015: 61-66 - [c115]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Comparing Prediction Models for Active Learning in Recommender Systems. LWA 2015: 171-180 - [c114]Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme:
Geo_ML @ MediaEval Placing Task 2015. MediaEval 2015 - [c113]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning Data Set Similarities for Hyperparameter Optimization Initializations. MetaSel@PKDD/ECML 2015: 15-26 - [c112]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) 2015: 87-103 - [c111]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. ECML/PKDD (2) 2015: 104-119 - [i7]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Discovery of Time-Series Shapelets. CoRR abs/1503.03238 (2015) - [i6]Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme:
Ultra-Fast Shapelets for Time Series Classification. CoRR abs/1503.05018 (2015) - [i5]Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme:
Optimal Time-Series Motifs. CoRR abs/1505.00423 (2015) - 2014
- [j9]Ruth Janning, André Busche, Tomás Horváth, Lars Schmidt-Thieme:
Buried pipe localization using an iterative geometric clustering on GPR data. Artif. Intell. Rev. 42(3): 403-425 (2014) - [j8]Josif Grabocka, Lars Schmidt-Thieme:
Invariant time-series factorization. Data Min. Knowl. Discov. 28(5-6): 1455-1479 (2014) - [c110]Lucas Rêgo Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Wolfgang Nejdl:
Optimizing Multi-Relational Factorization Models for Multiple Target Relations. CIKM 2014: 191-200 - [c109]Carlotta Schatten, Lars Schmidt-Thieme:
Adaptive Content Sequencing without Domain Information. CSEDU (1) 2014: 25-33 - [c108]Carlotta Schatten, Ruth Janning, Lars Schmidt-Thieme:
Vygotsky Based Sequencing Without Domain Information: A Matrix Factorization Approach. CSEDU (Selected Papers) 2014: 35-51 - [c107]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems. EC-TEL 2014: 179-192 - [c106]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Multimodal Affect Recognition for Adaptive Intelligent Tutoring Systems. EDM (Workshops) 2014 - [c105]Carlotta Schatten, Ruth Janning, Manolis Mavrikis, Lars Schmidt-Thieme:
Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems. EDM 2014: 385-386 - [c104]Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez Santos:
Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. ICALT 2014: 746-748 - [c103]Muhammad Umer Khan, Pavlos Basaras, Lars Schmidt-Thieme, Alexandros Nanopoulos, Dimitrios Katsaros:
Analyzing cooperative lane change models for connected vehicles. ICCVE 2014: 565-570 - [c102]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme, Gerhard Backfried, Norbert Pfannerer:
An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems. ICTAI 2014: 202-209 - [c101]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Automatic Subclasses Estimation for a Better Classification with HNNP. ISMIS 2014: 93-102 - [c100]Josif Grabocka, Alexandros Dalkalitsis, Athanasios Lois, Evangelos Katsaros, Lars Schmidt-Thieme:
Realistic optimal policies for energy-efficient train driving. ITSC 2014: 629-634 - [c99]Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning time-series shapelets. KDD 2014: 392-401 - [c98]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
Local Feature Extractors Accelerating HNNP for Phoneme Recognition. KI 2014: 231-242 - [c97]Rasoul Karimi, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Improved Questionnaire Trees for Active Learning in Recommender Systems. LWA 2014: 34-44 - [c96]Josif Grabocka, Erind Bedalli, Lars Schmidt-Thieme:
Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. PAKDD (1) 2014: 188-199 - [c95]Lucas Rêgo Drumond, Lars Schmidt-Thieme, Christoph Freudenthaler, Artus Krohn-Grimberghe:
Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification. PAKDD (1) 2014: 286-297 - [e5]Myra Spiliopoulou, Lars Schmidt-Thieme, Ruth Janning:
Data Analysis, Machine Learning and Knowledge Discovery - Proceedings of the 36th Annual Conference of the Gesellschaft für Klassifikation e. V., Hildesheim, Germany, August 2012. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2014, ISBN 978-3-319-01594-1 [contents] - 2013
- [c94]Josif Grabocka, Lucas Drumond, Lars Schmidt-Thieme:
Supervised Dimensionality Reduction via Nonlinear Target Estimation. DaWaK 2013: 172-183 - [c93]Muhammad Umer Khan, Alexandros Nanopoulos, Lars Schmidt-Thieme:
P2P RVM for Distributed Classification. ECDA 2013: 145-155 - [c92]Nicolas Schilling, André Busche, Simone Miller, Michael Jungheim, Martin Ptok, Lars Schmidt-Thieme:
Event Prediction in Pharyngeal High-Resolution Manometry. ECDA 2013: 341-352 - [c91]André Busche, Daniel Seyfried, Lars Schmidt-Thieme:
Hough Transform and Kirchhoff Migration for Supervised GPR Data Analysis. ECDA 2013: 489-499 - [c90]Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme:
HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information. ICTAI 2013: 24-29 - [c89]Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Factorized Decision Trees for Active Learning in Recommender Systems. ICTAI 2013: 404-411 - [c88]Martin Wistuba, Lars Schmidt-Thieme:
Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. KI 2013: 260-271 - [c87]André Busche, Ruth Janning, Lars Schmidt-Thieme:
Analysing the Potential Impact of Labeling Disagreements for Engineering Sensor Data. LWA 2013: 137-143 - [c86]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems. LWA 2013: 149-150 - [c85]Martin Wistuba, Lars Schmidt-Thieme:
Supervised Clustering of Social Media Streams. MediaEval 2013 - [c84]Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme:
Towards real-time collaborative filtering for big fast data. WWW (Companion Volume) 2013: 779-780 - [p4]Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme:
An Evaluation Measure for Learning from Imbalanced Data Based on Asymmetric Beta Distribution. Classification and Data Mining 2013: 121-129 - [i4]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Time-Series Classification Through Histograms of Symbolic Polynomials. CoRR abs/1307.6365 (2013) - [i3]Josif Grabocka, Lars Schmidt-Thieme:
Invariant Factorization Of Time-Series. CoRR abs/1312.6712 (2013) - 2012
- [b3]Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme, Gerd Stumme, Panagiotis Symeonidis:
Recommender Systems for Social Tagging Systems. Springer Briefs in Electrical and Computer Engineering, Springer 2012, ISBN 978-1-4614-1893-1, pp. i-ix, 1-111 - [j7]Krisztián Búza, Alexandros Nanopoulos, Tomás Horváth, Lars Schmidt-Thieme:
GRAMOFON: General model-selection framework based on networks. Neurocomputing 75(1): 163-170 (2012) - [c83]Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Classification of Sparse Time Series via Supervised Matrix Factorization. AAAI 2012: 929-934 - [c82]Ernesto Diaz-Aviles, Lucas Drumond, Zeno Gantner, Lars Schmidt-Thieme, Wolfgang Nejdl:
What is happening right now ... that interests me?: online topic discovery and recommendation in twitter. CIKM 2012: 1592-1596 - [c81]Christina Lichtenthäler, Lars Schmidt-Thieme:
Multinomial SVM Item Recommender for Repeat-Buying Scenarios. GfKl 2012: 189-197 - [c80]André Busche, Ruth Janning, Tomás Horváth, Lars Schmidt-Thieme:
A Unifying Framework for GPR Image Reconstruction. GfKl 2012: 325-332 - [c79]Josif Grabocka, Erind Bedalli, Lars Schmidt-Thieme:
Efficient Classification of Long Time-Series. ICT Innovations 2012: 47-57 - [c78]Ruth Janning, Tomás Horváth, André Busche, Lars Schmidt-Thieme:
GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing. AIAI (1) 2012: 347-356 - [c77]Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Invariant Time-Series Classification. ECML/PKDD (2) 2012: 725-740 - [c76]Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl:
Real-time top-n recommendation in social streams. RecSys 2012: 59-66 - [c75]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Exploiting the characteristics of matrix factorization for active learning in recommender systems. RecSys 2012: 317-320 - [c74]Lucas Drumond, Steffen Rendle, Lars Schmidt-Thieme:
Predicting RDF triples in incomplete knowledge bases with tensor factorization. SAC 2012: 326-331 - [c73]Lucas Drumond, Nguyen Thai-Nghe, Tomás Horváth, Lars Schmidt-Thieme:
Factorization techniques for student performance classification and ranking. UMAP Workshops 2012 - [c72]Nguyen Thai-Nghe, Lucas Drumond, Tomás Horváth, Lars Schmidt-Thieme:
Using factorization machines for student modeling. UMAP Workshops 2012 - [c71]Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme:
Multi-relational matrix factorization using bayesian personalized ranking for social network data. WSDM 2012: 173-182 - [c70]Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme:
Personalized Ranking for Non-Uniformly Sampled Items. KDD Cup 2012: 231-247 - [e4]Wolfgang Gaul, Andreas Geyer-Schulz, Lars Schmidt-Thieme, Jonas Kunze:
Challenges at the Interface of Data Analysis, Computer Science, and Optimization - Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21 - 23, 2010. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2012, ISBN 978-3-642-24465-0 [contents] - [i2]Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme:
BPR: Bayesian Personalized Ranking from Implicit Feedback. CoRR abs/1205.2618 (2012) - 2011
- [c69]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Active learning for aspect model in recommender systems. CIDM 2011: 162-167 - [c68]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
IQ estimation for accurate time-series classification. CIDM 2011: 216-223 - [c67]Nguyen Thai-Nghe, Lucas Drumond, Tomás Horváth, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Matrix and Tensor Factorization for Predicting Student Performance. CSEDU (1) 2011: 69-78 - [c66]Artus Krohn-Grimberghe, André Busche, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Active Learning for Technology Enhanced Learning. EC-TEL 2011: 512-518 - [c65]Nguyen Thai-Nghe, Tomás Horváth, Lars Schmidt-Thieme:
Factorization Models for Forecasting Student Performance. EDM 2011: 11-20 - [c64]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Fusion of Similarity Measures for Time Series Classification. HAIS (2) 2011: 253-261 - [c63]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme, Júlia Koller:
Fast Classification of Electrocardiograph Signals via Instance Selection. HISB 2011: 9-16 - [c62]Nguyen Thai-Nghe, Tomás Horváth, Lars Schmidt-Thieme:
Personalized Forecasting Student Performance. ICALT 2011: 412-414 - [c61]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems. ICTAI 2011: 1069-1076 - [c60]Timo Reuter, Philipp Cimiano, Lucas Drumond, Krisztián Búza, Lars Schmidt-Thieme:
Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. ICWSM 2011 - [c59]Christian Wartena, Wout Slakhorst, Martin Wibbels, Zeno Gantner, Christoph Freudenthaler, Chris Newell, Lars Schmidt-Thieme:
Keyword-Based TV Program Recommendation. ITWP@IJCAI 2011 - [c58]Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme:
A new evaluation measure for learning from imbalanced data. IJCNN 2011: 537-542 - [c57]Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Non-myopic active learning for recommender systems based on Matrix Factorization. IRI 2011: 299-303 - [c56]Artus Krohn-Grimberghe, André Busche, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Active Learning for Technology Enhanced Learning. LWA 2011: 28-31 - [c55]Rasoul Karimi, Alexandros Nanopoulos, Lars Schmidt-Thieme:
RFID-Enhanced Museum for Interactive Experience. MM4CH 2011: 192-205 - [c54]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. PAKDD (2) 2011: 149-160 - [c53]Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme:
MyMediaLite: a free recommender system library. RecSys 2011: 305-308 - [c52]Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme:
Fast context-aware recommendations with factorization machines. SIGIR 2011: 635-644 - [p3]Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme, Robert Jäschke, Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis:
Social Tagging Recommender Systems. Recommender Systems Handbook 2011: 615-644 - 2010
- [c51]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Time-Series Classification Based on Individualised Error Prediction. CSE 2010: 48-54 - [c50]Artus Krohn-Grimberghe, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Integrating OLAP and recommender systems: an evaluation perspective. DOLAP 2010: 85-92 - [c49]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Individualized Error Estimation for Classification and Regression Models. GfKl 2010: 183-191 - [c48]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Graph-Based Model-Selection Framework for Large Ensembles. HAIS (1) 2010: 557-564 - [c47]Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme:
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. ICDM 2010: 176-185 - [c46]Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme:
Cost-sensitive learning methods for imbalanced data. IJCNN 2010: 1-8 - [c45]André Busche, Artus Krohn-Grimberghe, Lars Schmidt-Thieme:
Mining Music Playlogs for Next Song Recommendations. LWA 2010: 35-38 - [c44]Artus Krohn-Grimberghe, Alexandros Nanopoulos, Lars Schmidt-Thieme:
A Novel Multidimensional Framework for Evaluating Recommender Systems. LWA 2010: 113-120 - [c43]Christine Preisach, Leandro Balby Marinho, Lars Schmidt-Thieme:
Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems. PAKDD (1) 2010: 348-357 - [c42]Bart P. Knijnenburg, Lars Schmidt-Thieme, Dirk G. F. M. Bollen:
Workshop on user-centric evaluation of recommender systems and their interfaces. RecSys 2010: 383-384 - [c41]Steffen Rendle, Lars Schmidt-Thieme:
Pairwise interaction tensor factorization for personalized tag recommendation. WSDM 2010: 81-90 - [c40]Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme:
Factorizing personalized Markov chains for next-basket recommendation. WWW 2010: 811-820 - [c39]Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, Lars Schmidt-Thieme:
Recommender system for predicting student performance. RecSysTEL@RecSys 2010: 2811-2819
2000 – 2009
- 2009
- [c38]Zeno Gantner, Lars Schmidt-Thieme:
Automatic Content-Based Categorization of Wikipedia Articles. PWNLP@IJCNLP 2009: 32-37 - [c37]Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-Thieme:
Swarming to rank for information retrieval. GECCO 2009: 9-16 - [c36]Avare Stewart, Ernesto Diaz-Aviles, Wolfgang Nejdl, Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Cross-tagging for personalized open social networking. Hypertext 2009: 271-278 - [c35]Nguyen Thai-Nghe, André Busche, Lars Schmidt-Thieme:
Improving Academic Performance Prediction by Dealing with Class Imbalance. ISDA 2009: 878-883 - [c34]Steffen Rendle, Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Learning optimal ranking with tensor factorization for tag recommendation. KDD 2009: 727-736 - [c33]Steffen Rendle, Christine Preisach, Lars Schmidt-Thieme:
Learning to Extract Relations for Relational Classification. PAKDD 2009: 1062-1071 - [c32]Leandro Balby Marinho, Christine Preisach, Lars Schmidt-Thieme:
Relational Classification for Personalized Tag Recommendation. DC@PKDD/ECML 2009 - [c31]Steffen Rendle, Lars Schmidt-Thieme:
Factor Models for Tag Recommendation in BibSonomy. DC@PKDD/ECML 2009 - [c30]Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme:
BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009: 452-461 - [c29]Zeno Gantner, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme:
Optimal Ranking for Video Recommendation. UCMedia 2009: 255-258 - [e3]Lawrence D. Bergman, Alexander Tuzhilin, Robin D. Burke, Alexander Felfernig, Lars Schmidt-Thieme:
Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, October 23-25, 2009. ACM 2009, ISBN 978-1-60558-435-5 [contents] - 2008
- [j6]Robert Jäschke, Leandro Balby Marinho, Andreas Hotho, Lars Schmidt-Thieme, Gerd Stumme:
Tag recommendations in social bookmarking systems. AI Commun. 21(4): 231-247 (2008) - [j5]Christine Preisach, Lars Schmidt-Thieme:
Ensembles of relational classifiers. Knowl. Inf. Syst. 14(3): 249-272 (2008) - [c28]Krisztián Búza, Lars Schmidt-Thieme:
Motif-Based Classification of Time Series with Bayesian Networks and SVMs. GfKl 2008: 105-114 - [c27]Steffen Rendle, Lars Schmidt-Thieme:
Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference. ICDM 2008: 1001-1006 - [c26]Steffen Rendle, Lars Schmidt-Thieme:
Scaling Record Linkage to Non-uniform Distributed Class Sizes. PAKDD 2008: 308-319 - [c25]Steffen Rendle, Lars Schmidt-Thieme:
Online-updating regularized kernel matrix factorization models for large-scale recommender systems. RecSys 2008: 251-258 - [c24]Karen H. L. Tso-Sutter, Leandro Balby Marinho, Lars Schmidt-Thieme:
Tag-aware recommender systems by fusion of collaborative filtering algorithms. SAC 2008: 1995-1999 - [c23]Leandro Balby Marinho, Krisztián Búza, Lars Schmidt-Thieme:
Folksonomy-Based Collabulary Learning. ISWC 2008: 261-276 - [e2]Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme, Reinhold Decker:
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. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2008, ISBN 978-3-540-78239-1 [contents] - [i1]Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Cai-Nicolas Ziegler:
Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems. CoRR abs/0812.4460 (2008) - 2007
- [j4]Dominik Benz, Karen H. L. Tso, Lars Schmidt-Thieme:
Supporting collaborative hierarchical classification: Bookmarks as an example. Comput. Networks 51(16): 4574-4585 (2007) - [j3]Alexander Felfernig, Gerhard Friedrich, Lars Schmidt-Thieme:
Guest Editors' Introduction: Recommender Systems. IEEE Intell. Syst. 22(3): 18-21 (2007) - [c22]Steffen Rendle, Lars Schmidt-Thieme:
Information Integration of Partially Labeled Data. GfKl 2007: 171-179 - [c21]Stefan Hauger, Karen H. L. Tso, Lars Schmidt-Thieme:
Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem. GfKl 2007: 525-532 - [c20]Leandro Balby Marinho, Lars Schmidt-Thieme:
Collaborative Tag Recommendations. GfKl 2007: 533-540 - [c19]Manuel Stritt, Lars Schmidt-Thieme, Gerhard Poeppel:
Combining multi-distributed mixture models and bayesian networks for semi-supervised learning. ICMLA 2007: 354-362 - [c18]Robert Jäschke, Leandro Balby Marinho, Andreas Hotho, Lars Schmidt-Thieme, Gerd Stumme:
Tag Recommendations in Folksonomies. LWA 2007: 13-20 - [c17]Robert Jäschke, Leandro Balby Marinho, Andreas Hotho, Lars Schmidt-Thieme, Gerd Stumme:
Tag Recommendations in Folksonomies. PKDD 2007: 506-514 - 2006
- [j2]Karen H. L. Tso, Lars Schmidt-Thieme:
Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data. J. Comput. 1(4): 18-29 (2006) - [j1]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, Ingo Krossing, Andreas W. Liehr, Andreas Karwath, Robert Klöfkorn, Raphaël Pesché, Tobias C. Potjans, Michael C. Röttger, Lars Schmidt-Thieme, Gerhard Schneider, Björn Voß, Bernd Wiebelt, Peter Wienemann, Volker-Henning Winterer:
A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. Prax. Inf.verarb. Kommun. 29(2): 81-87 (2006) - [c16]Jochen Fischer, Zeno Gantner, Steffen Rendle, Manuel Stritt, Lars Schmidt-Thieme:
Ideas and Improvements for Semantic Wikis. ESWC 2006: 650-663 - [c15]Manuel Stritt, Karen H. L. Tso, Lars Schmidt-Thieme:
Attribute Aware Anonymous Recommender Systems. GfKl 2006: 497-504 - [c14]Christine Preisach, Lars Schmidt-Thieme:
Relational Ensemble Classification. ICDM 2006: 499-509 - [c13]Steffen Rendle, Lars Schmidt-Thieme:
Object Identification with Constraints. ICDM 2006: 1026-1031 - [c12]Karen H. L. Tso, Lars Schmidt-Thieme:
Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics. PAKDD 2006: 831-840 - [p2]Karen H. L. Tso, Lars Schmidt-Thieme:
Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data. Data Science and Classification 2006: 271-278 - 2005
- [c11]Jens Hartmann, Nenad Stojanovic, Rudi Studer, Lars Schmidt-Thieme:
Ontology-Based Query Refinement for Semantic Portals. From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments 2005: 41-50 - [c10]Peter Haase, Andreas Hotho, Lars Schmidt-Thieme, York Sure:
Collaborative and Usage-Driven Evolution of Personal Ontologies. ESWC 2005: 486-499 - [c9]Karen H. L. Tso, Lars Schmidt-Thieme:
Attribute-aware Collaborative Filtering. GfKl 2005: 614-621 - [c8]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 Böhm, André Schaefer, Lars Schmidt-Thieme, Eric Schwarzkopf, Nenad Stojanovic, Rudi Studer, Roland Vollmar, Bernd Walter, Alexander Weber:
Fachinformationssystem Informatik (FIS-I) und Semantische Technologien für Informationsportale (SemIPort). GI Jahrestagung (2) 2005: 698-712 - [c7]Lars Schmidt-Thieme:
Compound Classification Models for Recommender Systems. ICDM 2005: 378-385 - [c6]Peter Haase, Andreas Hotho, Lars Schmidt-Thieme, York Sure:
Collaborative and Usage-driven Evolution of Personal Ontologies. LWA 2005: 151-157 - [c5]Ferenc Bodon, Lars Schmidt-Thieme:
The Relation of Closed Itemset Mining, Complete Pruning Strategies and Item Ordering in Apriori-Based FIM Algorithms. PKDD 2005: 437-444 - [p1]Lars Schmidt-Thieme, Martin Schader:
Performance Drivers for Depth-First Frequent Pattern Mining. Data Analysis and Decision Support 2005: 130-140 - [e1]Daniel Baier, Reinhold Decker, Lars Schmidt-Thieme:
Data Analysis and Decision Support. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2005, ISBN 978-3-540-26007-3 [contents] - 2004
- [c4]Cai-Nicolas Ziegler, Georg Lausen, Lars Schmidt-Thieme:
Taxonomy-driven computation of product recommendations. CIKM 2004: 406-415 - [c3]Lars Schmidt-Thieme:
Algorithmic Features of Eclat. FIMI 2004 - [c2]Christoph Breidert, Michael Hahsler, Lars Schmidt-Thieme:
Reservation Price Estimation by Adaptive Conjoint Analysis. GfKl 2004: 569-576 - 2003
- [b2]Martin Schader, Lars Schmidt-Thieme:
Java - eine Einführung (4. Aufl.). Springer 2003, ISBN 978-3-540-00663-3, pp. I-XVII, 1-634 - [b1]Lars Schmidt-Thieme:
Assoziationsregel-Algorithmen für Daten mit komplexer Struktur: mit Anwendungen im Web Mining. Karlsruhe Institute of Technology, 2003, ISBN 3-631-52213-4, pp. 1-158 - 2001
- [c1]Wolfgang Gaul, Lars Schmidt-Thieme:
Mining Generalized Association Rules for Sequential and Path Data. ICDM 2001: 593-596
Coauthor Index
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