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ORCIDStefan Kramer 0001
Person information
- affiliation: Johannes Gutenberg University Mainz, Institute of Computer Science, Germany
Other persons with the same name
- Stefan Kramer — disambiguation page
- Stefan Kramer 0002 — Daimler AG, Germany
- Stefan Kramer 0003 — TU Berlin, Germany
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2020 – today
- 2024
[c114]Cedric Derstroff, Mattia Cerrato, Jannis Brugger, Jan Peters, Stefan Kramer:
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations. AAAI 2024: 11766-11774- [i24]Cedric Derstroff, Jannis Brugger, Jannis Blüml, Mira Mezini, Stefan Kramer, Kristian Kersting:
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown. CoRR abs/2402.08511 (2024) - [i23]Mattia Cerrato, Marius Köppel, Philipp Wolf, Stefan Kramer:
10 Years of Fair Representations: Challenges and Opportunities. CoRR abs/2407.03834 (2024) - [i22]Derian Boer, Fabian Koch, Stefan Kramer:
Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answering. CoRR abs/2409.00861 (2024) - 2023
- [c113]Mattia Cerrato, Marius Köppel, Roberto Esposito, Stefan Kramer:
Invariant Representations with Stochastically Quantized Neural Networks. AAAI 2023: 6962-6970 - [c112]Lukas-Malte Bammert, Stefan Kramer, Mattia Cerrato, Ernst Althaus:
Privacy-Preserving Learning of Random Forests Without Revealing the Trees. DS 2023: 372-386 - [c111]Julian Vexler
, Stefan Kramer:
Classifying Aircraft Categories from Magnetometry Data Using a Hypotheses-Based Multi-Task Framework. ECAI 2023: 3241-3248 - [c110]Julian Vexler, Stefan Kramer:
Identifying Aircraft Motions and Patterns from Magnetometry Data Using a Knowledge-Based Multi-Fusion Approach. FUSION 2023: 1-8 - [i21]Stefan Kramer, Mattia Cerrato, Saso Dzeroski, Ross D. King:
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems. CoRR abs/2305.02251 (2023) - [i20]Cedric Derstroff, Mattia Cerrato, Jannis Brugger, Jan Peters, Stefan Kramer:
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations. CoRR abs/2312.09950 (2023) - 2022
- [c109]Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer:
Ranking Creative Language Characteristics in Small Data Scenarios. ICCC 2022: 136-140 - [i19]Mattia Cerrato, Marius Köppel, Alexander Segner, Stefan Kramer:
Fair Group-Shared Representations with Normalizing Flows. CoRR abs/2201.06336 (2022) - [i18]Mattia Cerrato, Marius Köppel, Alexander Segner, Stefan Kramer:
Fair Interpretable Learning via Correction Vectors. CoRR abs/2201.06343 (2022) - [i17]Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel, Alexander Segner, Roberto Esposito, Stefan Kramer:
Fair Interpretable Representation Learning with Correction Vectors. CoRR abs/2202.03078 (2022) - [i16]Mattia Cerrato, Marius Köppel, Roberto Esposito, Stefan Kramer:
Invariant Representations with Stochastically Quantized Neural Networks. CoRR abs/2208.02656 (2022) - [i15]Lukas Pensel, Stefan Kramer:
Neural RELAGGS. CoRR abs/2211.02363 (2022) - 2021
- [j48]Sophie Burkhardt, Jannis Brugger, Nicolas Wagner, Zahra Ahmadi, Kristian Kersting, Stefan Kramer:
Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation. Frontiers Artif. Intell. 4: 642263 (2021) - [c108]Stefan Kramer:
Myths and Misconceptions about Machine Learning and How They Are Related to Software Engineering. ENASE 2021: 5 - [c107]Weichen Li, Patrick Abels, Zahra Ahmadi, Sophie Burkhardt, Benjamin Schiller, Iryna Gurevych, Stefan Kramer:
Topic-Guided Knowledge Graph Construction for Argument Mining. ICBK 2021: 315-322 - [c106]Stefan Kramer:
Myths and Misconceptions about Machine Learning and How They Are Related to Software Engineering. ICEIS (1) 2021: 7 - [e5]Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I. Lecture Notes in Computer Science 12975, Springer 2021, ISBN 978-3-030-86485-9 [contents] - [e4]Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II. Lecture Notes in Computer Science 12976, Springer 2021, ISBN 978-3-030-86519-1 [contents] - [e3]Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part III. Lecture Notes in Computer Science 12977, Springer 2021, ISBN 978-3-030-86522-1 [contents] - [i14]Julia Siekiera, Stefan Kramer:
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data. CoRR abs/2101.00004 (2021) - [i13]Antoine Garcon, Julian Vexler, Dmitry Budker, Stefan Kramer:
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series. CoRR abs/2101.03850 (2021) - [i12]Patrick Abels, Zahra Ahmadi, Sophie Burkhardt, Benjamin Schiller, Iryna Gurevych, Stefan Kramer:
Focusing Knowledge-based Graph Argument Mining via Topic Modeling. CoRR abs/2102.02086 (2021) - [i11]Atif Raza, Stefan Kramer:
Pattern Sampling for Shapelet-based Time Series Classification. CoRR abs/2102.08498 (2021) - [i10]Ernst Althaus, Mohammad Sadeq Dousti, Stefan Kramer:
Fast Private Parameter Learning and Evaluation for Sum-Product Networks. CoRR abs/2104.07353 (2021) - 2020
- [j47]Atif Raza, Stefan Kramer:
Accelerating pattern-based time series classification: a linear time and space string mining approach. Knowl. Inf. Syst. 62(3): 1113-1141 (2020) - [c105]Mattia Cerrato, Marius Köppel, Alexander Segner, Roberto Esposito, Stefan Kramer:
Fair pairwise learning to rank. DSAA 2020: 729-738 - [c104]Stefan Kramer:
A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward). IJCAI 2020: 4868-4876 - [c103]Sophie Burkhardt, Julia Siekiera, Josua Glodde, Miguel A. Andrade-Navarro, Stefan Kramer:
Towards Identifying Drug Side Effects from Social Media Using Active Learning andCrowd Sourcing. PSB 2020: 319-330 - [i9]Hermann Kaindl, Stefan Kramer:
Towards Probability-based Safety Verification of Systems with Components from Machine Learning. CoRR abs/2003.01155 (2020) - [i8]Derian Boer, Stefan Kramer:
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning. CoRR abs/2006.02894 (2020) - [i7]Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer:
Ranking Creative Language Characteristics in Small Data Scenarios. CoRR abs/2010.12613 (2020) - [i6]Sophie Burkhardt, Jannis Brugger, Nicolas Wagner, Zahra Ahmadi, Kristian Kersting, Stefan Kramer:
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation. CoRR abs/2012.08459 (2020)
2010 – 2019
- 2019
- [j46]Sophie Burkhardt, Stefan Kramer:
Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model. J. Mach. Learn. Res. 20: 131:1-131:27 (2019) - [j45]Sophie Burkhardt, Stefan Kramer:
Multi-label classification using stacked hierarchical Dirichlet processes with reduced sampling complexity. Knowl. Inf. Syst. 59(1): 93-115 (2019) - [j44]Sophie Burkhardt, Stefan Kramer:
A Survey of Multi-Label Topic Models. SIGKDD Explor. 21(2): 61-79 (2019) - [c102]Sriparna Saha, Debanjan Sarkar, Stefan Kramer:
Exploring Multi-Objective Optimization for Multi-Label Classifier Ensembles. CEC 2019: 2753-2760 - [c101]Julian Vexler, Stefan Kramer:
Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption. DS 2019: 533-543 - [c100]Zahra Ahmadi, Stefan Kramer:
Modeling Multi-label Recurrence in Data Streams. ICBK 2019: 9-16 - [c99]Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer:
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. ECML/PKDD (3) 2019: 237-252 - [c98]Lukas Pensel, Stefan Kramer:
Forecast of Study Success in the STEM Disciplines Based Solely on Academic Records. PKDD/ECML Workshops (1) 2019: 647-657 - [e2]Carlos Alzate, Anna Monreale, Haytham Assem, Albert Bifet, Teodora Sandra Buda, Bora Caglayan, Brett Drury, Eva García-Martín, Ricard Gavaldà, Stefan Kramer, Niklas Lavesson, Michael Madden, Ian M. Molloy, Maria-Irina Nicolae, Mathieu Sinn:
ECML PKDD 2018 Workshops - Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings. Lecture Notes in Computer Science 11329, Springer 2019, ISBN 978-3-030-13452-5 [contents] - [i5]Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer:
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance. CoRR abs/1909.02768 (2019) - 2018
- [j43]Michael Geilke, Andreas Karwath, Eibe Frank, Stefan Kramer:
Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Min. Knowl. Discov. 32(3): 561-603 (2018) - [j42]Zahra Ahmadi, Stefan Kramer:
Modeling recurring concepts in data streams: a graph-based framework. Knowl. Inf. Syst. 55(1): 15-44 (2018) - [j41]Sophie Burkhardt, Stefan Kramer:
Online multi-label dependency topic models for text classification. Mach. Learn. 107(5): 859-886 (2018) - [j40]Zahra Ahmadi, Stefan Kramer:
A label compression method for online multi-label classification. Pattern Recognit. Lett. 111: 64-71 (2018) - [j39]Sriparna Saha, Sayantan Mitra, Stefan Kramer:
Exploring Multiobjective Optimization for Multiview Clustering. ACM Trans. Knowl. Discov. Data 12(4): 44:1-44:30 (2018) - [c97]Junming Shao, Qinli Yang, Zhong Zhang, Jinhu Liu, Stefan Kramer:
Graph Clustering with Local Density-Cut. DASFAA (1) 2018: 187-202 - [c96]Patrick Rehn, Zahra Ahmadi, Stefan Kramer:
Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data. DSAA 2018: 199-208 - [c95]Zahra Ahmadi, Peter Martens, Christopher Koch, Thomas Gottron, Stefan Kramer:
Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports. DSAA 2018: 293-302 - [c94]Robin Kobus, Adrian Lamoth, André Müller, Christian Hundt, Stefan Kramer, Bertil Schmidt:
cuBool: Bit-Parallel Boolean Matrix Factorization on CUDA-Enabled Accelerators. ICPADS 2018: 465-472 - [c93]Derian Boer, Zahra Ahmadi, Stefan Kramer:
Privacy Preserving Client/Vertical-Servers Classification. MIDAS/PAP@PKDD/ECML 2018: 125-140 - [c92]Hermann Kaindl, Stefan Kramer, Ralph Hoch:
An inductive learning perspective on automated generation of feature models from given product specifications. SPLC 2018: 25-30 - [i4]Zahra Ahmadi, Stefan Kramer:
Online Multi-Label Classification: A Label Compression Method. CoRR abs/1804.01491 (2018) - 2017
- [j38]Jörg Wicker, Stefan Kramer:
The best privacy defense is a good privacy offense: obfuscating a search engine user's profile. Data Min. Knowl. Discov. 31(5): 1419-1443 (2017) - [c91]Andreas Karwath, Markus Hubrich, Stefan Kramer:
Convolutional Neural Networks for the Identification of Regions of Interest in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer's Disease. AIME 2017: 316-321 - [c90]Zahra Ahmadi, Marcin Skowron, Aleksandrs Stier, Stefan Kramer:
An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling. DS 2017: 144-152 - [c89]Zahra Ahmadi, Aleksandrs Stier, Marcin Skowron, Stefan Kramer:
To Parse or Not to Parse: An Experimental Comparison of RNTNs and CNNs for Sentiment Analysis. EMSASW@ESWC 2017 - [c88]Sophie Burkhardt, Stefan Kramer:
Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity. ICBK 2017: 1-8 - [c87]Michael Geilke, Stefan Kramer:
Privacy-Preserving Pattern Mining on Online Density Estimates. ICBK 2017: 25-32 - [c86]Sophie Burkhardt, Stefan Kramer:
Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models. ECML/PKDD (2) 2017: 189-204 - [r2]Stefan Kramer:
Inductive Database Approach to Graphmining. Encyclopedia of Machine Learning and Data Mining 2017: 641-642 - [i3]Atif Raza, Stefan Kramer:
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs. CoRR abs/1702.06712 (2017) - 2016
- [j37]Martin Gütlein, Stefan Kramer:
Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability. J. Cheminformatics 8(1): 60:1-60:16 (2016) - [j36]Jörg Wicker, Tim Lorsbach, Martin Gütlein, Emanuel Schmid, Diogo Latino, Stefan Kramer, Kathrin Fenner:
enviPath - The environmental contaminant biotransformation pathway resource. Nucleic Acids Res. 44(Database-Issue): 502-508 (2016) - [j35]Junming Shao, Qinli Yang, Hoang-Vu Dang, Bertil Schmidt, Stefan Kramer:
Scalable Clustering by Iterative Partitioning and Point Attractor Representation. ACM Trans. Knowl. Discov. Data 11(1): 5:1-5:23 (2016) - [c85]Jörg Wicker, Andrey Tyukin, Stefan Kramer:
A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders. PAKDD (1) 2016: 328-340 - [c84]Michael Geilke, Andreas Karwath, Stefan Kramer:
Online Density Estimation of Heterogeneous Data Streams in Higher Dimensions. ECML/PKDD (1) 2016: 65-80 - [c83]Atif Raza, Jörg Wicker, Stefan Kramer:
Trading off accuracy for efficiency by randomized greedy warping. SAC 2016: 883-890 - [p2]Jörg Wicker, Kathrin Fenner, Stefan Kramer:
A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction. Computational Sustainability 2016: 75-97 - [i2]Junming Shao, Qinli Yang, Jinhu Liu, Stefan Kramer:
Graph Clustering with Density-Cut. CoRR abs/1606.00950 (2016) - 2015
- [j34]Rui Li, Robert Perneczky, Alexander Drzezga, Stefan Kramer:
Efficient redundancy reduced subgroup discovery via quadratic programming. J. Intell. Inf. Syst. 44(2): 271-288 (2015) - [c82]Michael Geilke, Andreas Karwath, Stefan Kramer:
Modeling recurrent distributions in streams using possible worlds. DSAA 2015: 1-9 - [c81]Jörg Wicker, Nicolas Krauter, Bettina Derstorff, Christof Stönner, Efstratios Bourtsoukidis, Thomas Klüpfel, Jonathan Williams, Stefan Kramer:
Cinema Data Mining: The Smell of Fear. KDD 2015: 1295-1304 - [c80]Andrey Tyukin, Stefan Kramer, Jörg Wicker:
Scavenger - A Framework for Efficient Evaluation of Dynamic and Modular Algorithms. ECML/PKDD (3) 2015: 325-328 - [c79]Eibe Frank, Michael Mayo, Stefan Kramer:
Alternating model trees. SAC 2015: 871-878 - [c78]Sophie Burkhardt, Stefan Kramer:
On the spectrum between binary relevance and classifier chains in multi-label classification. SAC 2015: 885-892 - 2014
- [j33]Martin Gütlein, Andreas Karwath, Stefan Kramer:
CheS-Mapper 2.0 for visual validation of (Q)SAR models. J. Cheminformatics 6(1): 41 (2014) - [j32]Jana Schmidt, Stefan Kramer:
Online Induction of Probabilistic Real-Time Automata. J. Comput. Sci. Technol. 29(3): 345-360 (2014) - [j31]Andreas Hapfelmeier, Bernhard Pfahringer, Stefan Kramer:
Pruning Incremental Linear Model Trees with Approximate Lookahead. IEEE Trans. Knowl. Data Eng. 26(8): 2072-2076 (2014) - [c77]Michael Geilke, Andreas Karwath, Stefan Kramer:
A probabilistic condensed representation of data for stream mining. DSAA 2014: 297-303 - [c76]Rui Li, Zahra Ahmadi, Stefan Kramer:
Constrained Latent Dirichlet Allocation for Subgroup Discovery with Topic Rules. ECAI 2014: 519-524 - [c75]Junming Shao, Zahra Ahmadi, Stefan Kramer:
Prototype-based learning on concept-drifting data streams. KDD 2014: 412-421 - [c74]Andrey Tyukin, Stefan Kramer, Jörg Wicker:
BMaD - A Boolean Matrix Decomposition Framework. ECML/PKDD (3) 2014: 481-484 - [c73]Madeleine Seeland, Andreas Karwath, Stefan Kramer:
Structural clustering of millions of molecular graphs. SAC 2014: 121-128 - [c72]Madeleine Seeland, Andreas Maunz, Andreas Karwath, Stefan Kramer:
Extracting information from support vector machines for pattern-based classification. SAC 2014: 129-136 - 2013
- [j30]Tobias Girschick, Ulrich Rückert, Stefan Kramer:
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships and Data-Driven Selection of Source Datasets. Comput. J. 56(3): 274-288 (2013) - [j29]Jana Schmidt, Asghar Ghorbani, Andreas Hapfelmeier, Stefan Kramer:
Learning probabilistic real-time automata from multi-attribute event logs. Intell. Data Anal. 17(1): 93-123 (2013) - [j28]Tobias Girschick, Lucia Puchbauer, Stefan Kramer:
Improving structural similarity based virtual screening using background knowledge. J. Cheminformatics 5: 50 (2013) - [j27]Tobias Girschick, Pedro R. Almeida, Stefan Kramer, Jonna C. Stålring:
Similarity Boosted Quantitative Structure-Activity Relationship - A Systematic Study of Enhancing Structural Descriptors by Molecular Similarity. J. Chem. Inf. Model. 53(5): 1017-1025 (2013) - [c71]Michael Geilke, Eibe Frank, Andreas Karwath, Stefan Kramer:
Online Estimation of Discrete Densities. ICDM 2013: 191-200 - [c70]Andreas Hapfelmeier, Jana Schmidt, Stefan Kramer:
Incremental linear model trees on massive datasets: keep it simple, keep it fast. SAC 2013: 129-135 - [c69]Madeleine Seeland, Stefan Kramer, Bernhard Pfahringer:
Model selection based product kernel learning for regression on graphs. SAC 2013: 136-143 - 2012
- [j26]Zejun Zheng, Stefan Kramer, Bertil Schmidt:
DySC: software for greedy clustering of 16S rRNA reads. Bioinform. 28(16): 2182-2183 (2012) - [j25]Martin Gütlein, Andreas Karwath, Stefan Kramer:
CheS-Mapper - Chemical Space Mapping and Visualization in 3D. J. Cheminformatics 4: 7 (2012) - [c68]Rui Li, Stefan Kramer:
Efficient Redundancy Reduced Subgroup Discovery via Quadratic Programming. Discovery Science 2012: 125-138 - [c67]Jana Schmidt, Stefan Kramer:
Online Induction of Probabilistic Real Time Automata. ICDM 2012: 625-634 - [c66]Madeleine Seeland, Andreas Karwath, Stefan Kramer:
A structural cluster kernel for learning on graphs. KDD 2012: 516-524 - [c65]Madeleine Seeland, Fabian Buchwald, Stefan Kramer, Bernhard Pfahringer:
Maximum Common Subgraph based locally weighted regression. SAC 2012: 165-172 - [c64]Jörg Wicker, Bernhard Pfahringer, Stefan Kramer:
Multi-label classification using boolean matrix decomposition. SAC 2012: 179-186 - [c63]Jana Schmidt, Sonja Ansorge, Stefan Kramer:
Scalable Induction of Probabilistic Real-Time Automata Using Maximum Frequent Pattern Based Clustering. SDM 2012: 272-283 - 2011
- [j24]Tobias Hamp, Fabian Birzele, Fabian Buchwald, Stefan Kramer:
Improving structure alignment-based prediction of SCOP families using Vorolign Kernels. Bioinform. 27(2): 204-210 (2011) - [j23]Fabian Buchwald, Lothar Richter, Stefan Kramer:
Predicting a small molecule-kinase interaction map: A machine learning approach. J. Cheminformatics 3: 22 (2011) - [j22]Andreas Maunz, Christoph Helma, Stefan Kramer:
Efficient mining for structurally diverse subgraph patterns in large molecular databases. Mach. Learn. 83(2): 193-218 (2011) - [c62]Rui Li, Andreas Hapfelmeier, Jana Schmidt, Robert Perneczky, Alexander Drzezga, Alexander Kurz, Stefan Kramer:
A Case Study of Stacked Multi-view Learning in Dementia Research. AIME 2011: 60-69 - [c61]Jana Schmidt, Stefan Kramer:
The Augmented Itemset Tree: A Data Structure for Online Maximum Frequent Pattern Mining. Discovery Science 2011: 277-291 - [c60]Jana Schmidt, Elisabeth Maria Brändle, Stefan Kramer:
Clustering with Attribute-Level Constraints. ICDM 2011: 1206-1211 - [c59]Madeleine Seeland, Simon A. Berger, Alexandros Stamatakis, Stefan Kramer:
Parallel Structural Graph Clustering. ECML/PKDD (3) 2011: 256-272 - 2010
- [j21]Pawel Smialowski, Dmitrij Frishman, Stefan Kramer:
Pitfalls of supervised feature selection. Bioinform. 26(3): 440-443 (2010) - [j20]Jörg Wicker, Kathrin Fenner, Lynda B. M. Ellis, Lawrence P. Wackett, Stefan Kramer:
Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach. Bioinform. 26(6): 814-821 (2010) - [j19]Barry J. Hardy, Nicki Douglas, Christoph Helma, Micha Rautenberg, Nina Jeliazkova, Vedrin Jeliazkov, Ivelina Nikolova, Romualdo Benigni, Olga Tcheremenskaia, Stefan Kramer, Tobias Girschick, Fabian Buchwald, Jörg Wicker, Andreas Karwath, Martin Gütlein, Andreas Maunz, Haralambos Sarimveis, Georgia Melagraki, Antreas Afantitis, Pantelis Sopasakis, David Gallagher, Vladimir Poroikov, Dmitry Filimonov, Alexey V. Zakharov, Alexey Lagunin, Tatyana Gloriozova, Sergey Novikov, Natalia Skvortsova, Dmitry S. Druzhilovskiy, Sunil Chawla, Indira Ghosh, Surajit Ray, Hitesh Patel, Sylvia Escher:
Collaborative development of predictive toxicology applications. J. Cheminformatics 2: 7 (2010) - [j18]Jana Schmidt, Andreas Hapfelmeier, Marianne Mueller, Robert Perneczky, Alexander Kurz, Alexander Drzezga, Stefan Kramer:
Interpreting PET scans by structured patient data: a data mining case study in dementia research. Knowl. Inf. Syst. 24(1): 149-170 (2010) - [j17]Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan Kramer:
A Study of Hierarchical and Flat Classification of Proteins. IEEE ACM Trans. Comput. Biol. Bioinform. 7(3): 563-571 (2010) - [c58]Fabian Buchwald, Tobias Girschick, Eibe Frank, Stefan Kramer:
Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships. AAAI 2010: 1268-1273 - [c57]Marianne Mueller, Stefan Kramer:
Integer Linear Programming Models for Constrained Clustering. Discovery Science 2010: 159-173 - [c56]Steven Ganzert, Josef Guttmann, Daniel Steinmann, Stefan Kramer:
Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung. Discovery Science 2010: 296-310 - [c55]Thomas Hopf, Stefan Kramer:
Mining Class-Correlated Patterns for Sequence Labeling. Discovery Science 2010: 311-325 - [c54]Ulrich Rückert, Tobias Girschick, Fabian Buchwald, Stefan Kramer:
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships. Discovery Science 2010: 341-355 - [c53]Érick Alphonse, Tobias Girschick, Fabian Buchwald, Stefan Kramer:
A Numerical Refinement Operator Based on Multi-Instance Learning. ILP 2010: 14-21 - [c52]Stefan Kramer:
Learning Real-Time Automata from Multi-Attribute Event Logs. NyNaK 2010 - [c51]Madeleine Seeland, Tobias Girschick, Fabian Buchwald, Stefan Kramer:
Online Structural Graph Clustering Using Frequent Subgraph Mining. ECML/PKDD (3) 2010: 213-228 - [c50]Andreas Maunz, Christoph Helma, Tobias Cramer, Stefan Kramer:
Latent Structure Pattern Mining. ECML/PKDD (2) 2010: 353-368 - [p1]Jörg Wicker, Lothar Richter, Stefan Kramer:
SINDBAD and SiQL: Overview, Applications and Future Developments. Inductive Databases and Constraint-Based Data Mining 2010: 289-309 - [r1]Stefan Kramer:
Inductive Database Approach to Graphmining. Encyclopedia of Machine Learning 2010: 522-523
2000 – 2009
- 2009
- [j16]Timo Duchrow, Timur Shtatland, Daniel Guettler, Misha Pivovarov, Stefan Kramer, Ralph Weissleder:
Enhancing navigation in biomedical databases by community voting and database-driven text classification. BMC Bioinform. 10: 317 (2009) - [c49]Steven Ganzert, Stefan Kramer, Knut Möller, Daniel Steinmann, Josef Guttmann:
Prediction of Mechanical Lung Parameters Using Gaussian Process Models. AIME 2009: 380-384 - [c48]Marianne Mueller, Rómer Rosales, Harald Steck, Sriram Krishnan, Bharat Rao, Stefan Kramer:
Data-Efficient Information-Theoretic Test Selection. AIME 2009: 410-415 - [c47]Marianne Mueller, Rómer Rosales, Harald Steck, Sriram Krishnan, Bharat Rao, Stefan Kramer:
Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis. IDA 2009: 119-130 - [c46]Lothar Richter, Regina Augustin, Stefan Kramer:
Finding Relational Associations in HIV Resistance Mutation Data. ILP 2009: 202-208 - [c45]Andreas Maunz, Christoph Helma, Stefan Kramer:
Large-scale graph mining using backbone refinement classes. KDD 2009: 617-626 - 2008
- [j15]Kathrin Fenner, Junfeng Gao, Stefan Kramer, Lynda B. M. Ellis, Lawrence P. Wackett:
Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction. Bioinform. 24(18): 2079-2085 (2008) - [j14]Ulrich Rückert, Stefan Kramer:
Margin-based first-order rule learning. Mach. Learn. 70(2-3): 189-206 (2008) - [j13]Sebastian Fröhler, Stefan Kramer:
Inductive logic programming for gene regulation prediction. Mach. Learn. 70(2-3): 225-240 (2008) - [c44]Lothar Richter, Jörg Wicker, Kristina Kessler, Stefan Kramer:
An inductive database and query language in the relational model. EDBT 2008: 740-744 - [c43]Andreas Hapfelmeier, Jana Schmidt, Marianne Mueller, Stefan Kramer, Robert Perneczky, Alexander Kurz, Alexander Drzezga:
Interpreting PET Scans by Structured Patient Data: A Data Mining Case Study in Dementia Research. ICDM 2008: 213-222 - [c42]Ulrich Rückert, Stefan Kramer:
Kernel-Based Inductive Transfer. ECML/PKDD (2) 2008: 220-233 - [c41]Jörg Wicker, Lothar Richter, Kristina Kessler, Stefan Kramer:
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model. ECML/PKDD (2) 2008: 690-694 - 2007
- [j12]Selina Sommer, Stefan Kramer:
Three Data Mining Techniques To Improve Lazy Structure-Activity Relationships for Noncongeneric Compounds. J. Chem. Inf. Model. 47(6): 2035-2043 (2007) - [c40]Ulrich Rückert, Stefan Kramer:
Optimizing Feature Sets for Structured Data. ECML 2007: 716-723 - 2006
- [j11]Fabian Birzele, Stefan Kramer:
A new representation for protein secondary structure prediction based on frequent patterns. Bioinform. 22(21): 2628-2634 (2006) - [j10]Hendrik Blockeel, David D. Jensen, Stefan Kramer:
Introduction to the special issue on multi-relational data mining and statistical relational learning. Mach. Learn. 62(1-2): 3-5 (2006) - [c39]Lothar Richter, Stefan Hechtl, Stefan Kramer:
Leveraging Chemical Background Knowledge for the Prediction of Growth Inhibition. BIBE 2006: 319-324 - [c38]Ulrich Rückert, Stefan Kramer:
A statistical approach to rule learning. ICML 2006: 785-792 - [c37]Sebastian Fröhler, Stefan Kramer:
Inductive Logic Programming for Gene Regulation Prediction. ILP 2006: 34-36 - [c36]Ulrich Rückert, Stefan Kramer:
Margin-Based First-Order Rule Learning. ILP 2006: 46-48 - [c35]Johannes Fischer, Volker Heun, Stefan Kramer:
Optimal String Mining Under Frequency Constraints. PKDD 2006: 139-150 - [c34]Lothar Richter, Ulrich Rückert, Stefan Kramer:
Learning a Predictive Model for Growth Inhibition from the NCI DTP Human Tumor Cell Line Screening Data: Does Gene Expression Make a Difference? Pacific Symposium on Biocomputing 2006: 596-607 - 2005
- [c33]Elisabeth Georgii, Lothar Richter, Ulrich Rückert, Stefan Kramer:
Analyzing microarray data using quantitative association rules. ECCB/JBI 2005: 129 - [c32]Johannes Fischer, Volker Heun, Stefan Kramer:
Fast Frequent String Mining Using Suffix Arrays. ICDM 2005: 609-612 - [c31]Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow, Jörg Wicker, Lothar Richter:
Inductive Databases in the Relational Model: The Data as the Bridge. KDID 2005: 124-138 - [c30]Vahan Harput, Hermann Kaindl, Stefan Kramer:
Extending Function Point Analysis of Object-Oriented Requirements Specifications. IEEE METRICS 2005: 39 - [c29]Lin Dong, Eibe Frank, Stefan Kramer:
Ensembles of Balanced Nested Dichotomies for Multi-class Problems. PKDD 2005: 84-95 - [e1]Stefan Kramer, Bernhard Pfahringer:
Inductive Logic Programming, 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005, Proceedings. Lecture Notes in Computer Science 3625, Springer 2005, ISBN 3-540-28177-0 [contents] - 2004
- [j9]Hendrik Blockeel, Saso Dzeroski, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer:
Experiments In Predicting Biodegradability. Appl. Artif. Intell. 18(2): 157-181 (2004) - [j8]Christoph Helma, Tobias Cramer, Stefan Kramer, Luc De Raedt:
Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds. J. Chem. Inf. Model. 44(4): 1402-1411 (2004) - [j7]Stefan Kramer, Hermann Kaindl:
Coupling and cohesion metrics for knowledge-based systems using frames and rules. ACM Trans. Softw. Eng. Methodol. 13(3): 332-358 (2004) - [c28]Ulrich Rückert, Lothar Richter, Stefan Kramer:
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach. ICDM 2004: 507-510 - [c27]Eibe Frank, Stefan Kramer:
Ensembles of nested dichotomies for multi-class problems. ICML 2004 - [c26]Ulrich Rückert, Stefan Kramer:
Towards tight bounds for rule learning. ICML 2004 - [c25]Ulrich Rückert, Stefan Kramer:
Frequent free tree discovery in graph data. SAC 2004: 564-570 - [i1]Raghu Ramakrishnan, Rakesh Agrawal, Johann-Christoph Freytag, Toni Bollinger, Christopher W. Clifton, Saso Dzeroski, Jochen Hipp, Daniel A. Keim, Stefan Kramer, Hans-Peter Kriegel, Ulf Leser, Bing Liu, Heikki Mannila, Rosa Meo, Shinichi Morishita, Raymond T. Ng, Jian Pei, Prabhakar Raghavan, Myra Spiliopoulou, Jaideep Srivastava, Vicenç Torra:
Data Mining: The Next Generation. Perspectives Workshop: Data Mining: The Next Generation 2004 - 2003
- [j6]Christoph Helma, Stefan Kramer:
A Survey of the Predictive Toxicology Challenge 2000-2001. Bioinform. 19(10): 1179-1182 (2003) - [j5]Hannu Toivonen, Ashwin Srinivasan, Ross D. King, Stefan Kramer, Christoph Helma:
Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinform. 19(10): 1183-1193 (2003) - [c24]Hermann Kaindl, Stefan Kramer, Mario Hailing, Vahan Harput:
Metamodel-Compliance Checking of Requirements in a Semiformal Representation. CAiSE Short Paper Proceedings 2003 - [c23]Ulrich Rückert, Stefan Kramer:
Stochastic Local Search in k-Term DNF Learning. ICML 2003: 648-655 - [c22]Ulrich Rückert, Stefan Kramer:
Generalized Version Space Trees. KDID 2003: 119-129 - [c21]Kristian Kersting, Tapani Raiko, Stefan Kramer, Luc De Raedt:
Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models. Pacific Symposium on Biocomputing 2003: 192-203 - 2002
- [j4]Steven Ganzert, Josef Guttmann, Kristian Kersting, Ralf Kuhlen, Christian Putensen, Michael Sydow, Stefan Kramer:
Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning. Artif. Intell. Medicine 26(1-2): 69-86 (2002) - [c20]Ulrich Rückert, Stefan Kramer, Luc De Raedt:
Phase Transitions and Stochastic Local Search in k-Term DNF Learning. ECML 2002: 405-417 - [c19]Björn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer:
Transformation-Based Regression. ICML 2002: 59-66 - 2001
- [j3]Christoph Helma, Ross D. King, Stefan Kramer, Ashwin Srinivasan:
The Predictive Toxicology Challenge 2000-2001. Bioinform. 17(1): 107-108 (2001) - [j2]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. Fundam. Informaticae 47(1-2): 1-13 (2001) - [c18]Hermann Kaindl, Stefan Kramer, Mario Hailing:
An Interactive Guide Through a Defined Modelling Process. BCS HCI/IHM 2001: 107-123 - [c17]Stefan Kramer, Luc De Raedt:
Feature Construction with Version Spaces for Biochemical Applications. ICML 2001: 258-265 - [c16]Luc De Raedt, Stefan Kramer:
The Levelwise Version Space Algorithm and its Application to Molecular Fragment Finding. IJCAI 2001: 853-862 - [c15]Stefan Kramer:
Demand-Driven Construction of Structural Features in ILP. ILP 2001: 132-141 - [c14]Stefan Kramer, Luc De Raedt, Christoph Helma:
Molecular feature mining in HIV data. KDD 2001: 136-143 - 2000
- [j1]Stefan Kramer:
Thesis: Relational learning vs. propositionalization. AI Commun. 13(4): 275-276 (2000) - [c13]Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer:
Learning to Use Operational Advice. ECAI 2000: 291-295 - [c12]Stefan Kramer, Eibe Frank:
Bottom-Up Propositionalization. ILP Work-in-progress reports 2000 - [c11]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. ISMIS 2000: 426-434
1990 – 1999
- 1999
- [c10]Hermann Kaindl, Stefan Kramer, Papa Samba Niang Diallo:
Semiautomatic Generation of Glossary Links: A Practical Solution. Hypertext 1999: 3-12 - [c9]Saso Dzeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer:
Experiments in Predicting Biodegradability. ILP 1999: 80-91 - 1998
- [c8]Hermann Kaindl, Stefan Kramer, Luis Miguel Afonso:
Combining Structure Search and Content Search for the World-Wide Web. Hypertext 1998: 217-224 - [c7]Hermann Kaindl, Stefan Kramer, Robert Kacsich:
A Case Study of Decomposing Functional Requirements Using Scenarios. ICRE 1998: 156-163 - [c6]Stefan Kramer, Bernhard Pfahringer, Christoph Helma:
Stochastic Propositionalization of Non-determinate Background Knowledge. ILP 1998: 80-94 - 1997
- [c5]Stefan Kramer, Hermann Kaindl, Stefan Schlee:
Can We Benefit from Metrics in KBS Development? IJCAI (1) 1997: 662-667 - [c4]Stefan Kramer, Bernhard Pfahringer, Christoph Helma:
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail. KDD 1997: 223-226 - 1996
- [c3]Stefan Kramer:
Structural Regression Trees. AAAI/IAAI, Vol. 1 1996: 812-819 - [c2]Stefan Kramer, Bernhard Pfahringer:
Efficient Search for Strong Partial Determinations. KDD 1996: 371-374 - 1995
- [c1]Bernhard Pfahringer, Stefan Kramer:
Compression-Based Evaluation of Partial Determinations. KDD 1995: 234-239
Coauthor Index
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