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ORCIDLars Schmidt-Thieme
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- affiliation: University of Hildesheim
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2020 – today
- 2020
[c157]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- [c156]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 - [c155]Shayan Jawed, Josif Grabocka, Lars Schmidt-Thieme:
Self-supervised Learning for Semi-supervised Time Series Classification. PAKDD (1) 2020: 499-511 - [c154]Mohsan Jameel, Shayan Jawed, Lars Schmidt-Thieme:
Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD. PAKDD (2) 2020: 894-905 - [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. 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]Krisztian Buza, 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 - [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]