Barbara Hammer
172 publications
18 venues
H Index 25
Affiliation
Bielefeld University, Faculty of Technology, Bielefeld, GermanyClausthal University of Technology, Computer Science Institute, Clausthal-Zellerfeld, Germany
University of Osnabr ck, Institute of Computer Science, Osnabr ck, Germany
Links
DBLPGoogle Scholar
- https://www.techfak.uni-bielefeld.de/~bhammer/
- https://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=18302458&lang=en
- http://www.academia-net.org/profil/prof-dr-barbara-hammer/1203241
- https://orcid.org/0000-0002-0935-5591
- https://www.wikidata.org/entity/Q21255170
- https://www.wikidata.org/entity/Q78475288
- https://www.researcherid.com/rid/E-8624-2010
- http://isni.org/isni/0000000119467418
- https://id.loc.gov/authorities/names/n00010822
- https://d-nb.info/gnd/13377435X
- https://mathgenealogy.org/id.php?id=192648
| Name | Venue | Year | citations |
|---|---|---|---|
Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals.
|
ICANN | 2025 | 0 |
| Robust Individual Sensor Placement by Means of Domain-informed Adversarial Training.
|
IJCNN | 2025 | 0 |
| Energy Efficient Online Stream Classification under Concept Drift on FPGAs for Edge Computing.
|
IJCNN | 2025 | 0 |
| Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks.
|
ICLR | 2025 | 7 |
| Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems.
|
IJCNN | 2025 | 2 |
| Continuous Fair SMOTE - Fairness-Aware Stream Learning from Imbalanced Data.
|
ICANN | 2025 | 0 |
| Go with the Flow: Leveraging Physics-Informed Gradients to Solve Real-World Problems in Water Distribution Systems.
|
ECML/PKDD | 2025 | 0 |
| Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation.
|
Cognitive System Research | 2025 | 10 |
| Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory.
|
AISTATS | 2025 | 0 |
| Similarity-Based Zero-Shot Domain Adaptation for Wearables.
|
ESANN | 2024 | 0 |
| Federated Loss Exploration for Improved Convergence on Non-IID Data.
|
IJCNN | 2024 | 7 |
| Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection.
|
IJCNN | 2024 | 7 |
| Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations.
|
IJCNN | 2024 | 0 |
| shapiq: Shapley Interactions for Machine Learning.
|
NIPS/NeurIPS | 2024 | 40 |
| On the Fine Structure of Drifting Features.
|
ESANN | 2024 | 1 |
| FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation.
|
IJCNN | 2024 | 1 |
| Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
|
AAAI | 2024 | 32 |
| Visualizing and Improving 3D Mesh Segmentation with DeepView.
|
ESANN | 2024 | 1 |
| No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation.
|
IJCNN | 2024 | 0 |
| Physics-Informed Graph Neural Networks for Water Distribution Systems.
|
AAAI | 2024 | 25 |
| Self-Supervised Learning from Incrementally Drifting Data Streams.
|
ESANN | 2024 | 1 |
| Trust in Artificial Intelligence: Beyond Interpretability.
|
ESANN | 2024 | 2 |
| Machine learning in distributed, federated and non-stationary environments - recent trends.
|
ESANN | 2024 | 2 |
| SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification.
|
AISTATS | 2024 | 14 |
| Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty.
|
ESANN | 2024 | 0 |
| Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks.
|
ICANN | 2024 | 4 |
| The SAME score: Improved cosine based measure for semantic bias.
|
IJCNN | 2024 | 0 |
| KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
|
ICML | 2024 | 0 |
| Noise Robust One-Class Intrusion Detection on Dynamic Graphs.
|
ESANN | 2024 | 0 |
| One-Class Intrusion Detection with Dynamic Graphs.
|
ICANN | 2023 | 1 |
| On Feature Removal for Explainability in Dynamic Environments.
|
ESANN | 2023 | 0 |
| iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.
|
ECML/PKDD | 2023 | 6 |
| SHAP-IQ: Unified Approximation of any-order Shapley Interactions.
|
NIPS/NeurIPS | 2023 | 52 |
| Feature Selection for Concept Drift Detection.
|
ESANN | 2023 | 6 |
| Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts.
|
ESANN | 2023 | 0 |
| Faster Convergence for Transformer Fine-tuning with Line Search Methods.
|
IJCNN | 2023 | 2 |
| Incremental permutation feature importance (iPFI): towards online explanations on data streams.
|
MLJ | 2023 | 0 |
| From hyperspectral to multispectral sensing - from simulation to reality: A comprehensive approach for calibration model transfer.
|
ESANN | 2022 | 2 |
| Localization of Concept Drift: Identifying the Drifting Datapoints.
|
IJCNN | 2022 | 13 |
| Stream-Based Active Learning with Verification Latency in Non-stationary Environments.
|
ICANN | 2022 | 7 |
| SAM-kNN Regressor for Online Learning in Water Distribution Networks.
|
ICANN | 2022 | 1 |
| Feature Selection for Trustworthy Regression Using Higher Moments.
|
ICANN | 2022 | 2 |
| A Graph-based U-Net Model for Predicting Traffic in unseen Cities.
|
IJCNN | 2022 | 7 |
| Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks.
|
ICANN | 2022 | 8 |
| Improving Zorro Explanations for Sparse Observations with Dense Proxy Data.
|
ESANN | 2022 | 0 |
| Contrasting Explanation of Concept Drift.
|
ESANN | 2022 | 13 |
| Model Agnostic Local Explanations of Reject.
|
ESANN | 2022 | 8 |
| Federated learning vector quantization for dealing with drift between nodes.
|
ESANN | 2022 | 1 |
| Impact of different loss functions on denoising of microscopic images.
|
IJCNN | 2022 | 5 |
| Reject Options for Incremental Regression Scenarios.
|
ICANN | 2022 | 0 |
| Neural Architecture Search for Sentence Classification with BERT.
|
ESANN | 2022 | 0 |
| Machine Learning for Measuring and Analyzing Online Social Communications.
|
ESANN | 2021 | 0 |
| Concept Drift Segmentation via Kolmogorov-Trees.
|
ESANN | 2021 | 10 |
| Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting.
|
ESANN | 2021 | 4 |
| Graph Edit Networks.
|
ICLR | 2021 | 16 |
| Federated Learning Vector Quantization.
|
ESANN | 2021 | 5 |
| Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise.
|
ECML/PKDD | 2021 | 26 |
| Efficient computation of contrastive explanations.
|
IJCNN | 2021 | 0 |
| Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data.
|
ESANN | 2021 | 0 |
| Explaining Concept Drift by Mean of Direction.
|
ICANN | 2020 | 2 |
| Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data.
|
ICANN | 2020 | 7 |
| Locally Adaptive Nearest Neighbors.
|
ESANN | 2020 | 1 |
| Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).
|
ICML | 2020 | 28 |
| Sparse Metric Learning in Prototype-based Classification.
|
ESANN | 2020 | 0 |
| Convex Density Constraints for Computing Plausible Counterfactual Explanations.
|
ICANN | 2020 | 56 |
| Randomizing the Self-Adjusting Memory for Enhanced Handling of Concept Drift.
|
IJCNN | 2020 | 4 |
| Efficient computation of counterfactual explanations of LVQ models.
|
ESANN | 2020 | 0 |
| DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.
|
IJCAI | 2020 | 0 |
| Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning.
|
IJCNN | 2019 | 2 |
| Feature relevance bounds for ordinal regression.
|
ESANN | 2019 | 6 |
| Multiple-Kernel dictionary learning for reconstruction and clustering of unseen multivariate time-series.
|
ESANN | 2019 | 0 |
| Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets.
|
ESANN | 2019 | 10 |
| Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold.
|
ECML/PKDD | 2019 | 14 |
| Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation.
|
ICDM | 2019 | 23 |
| Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection.
|
CIKM | 2019 | 1 |
| Recovering Localized Adversarial Attacks.
|
ICANN | 2019 | 4 |
| Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units.
|
ICRA | 2019 | 4 |
| Tree Edit Distance Learning via Adaptive Symbol Embeddings.
|
ICML | 2018 | 24 |
| Differential private relevance learning.
|
ESANN | 2018 | 2 |
| Confident Kernel Sparse Coding and Dictionary Learning.
|
ICDM | 2018 | 3 |
| Mitigating Concept Drift via Rejection.
|
ICANN | 2018 | 16 |
| Enhancing Very Fast Decision Trees with Local Split-Time Predictions.
|
ICDM | 2018 | 8 |
| Feasibility based Large Margin Nearest Neighbor metric learning.
|
ESANN | 2018 | 0 |
| An EM transfer learning algorithm with applications in bionic hand prostheses.
|
ESANN | 2017 | 5 |
| Feature Relevance Bounds for Linear Classification.
|
ESANN | 2017 | 9 |
| Label-noise-tolerant classification for streaming data.
|
IJCNN | 2017 | 3 |
| Self-Adjusting Memory: How to Deal with Diverse Drift Types.
|
IJCAI | 2017 | 28 |
| Discriminative dimensionality reduction in kernel space.
|
ESANN | 2016 | 1 |
| Local Reject Option for Deterministic Multi-class SVM.
|
ICANN | 2016 | 7 |
| Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
|
ICANN | 2016 | 4 |
| Choosing the best algorithm for an incremental on-line learning task.
|
ESANN | 2016 | 18 |
| Gaussian process prediction for time series of structured data.
|
ESANN | 2016 | 6 |
| Incremental learning algorithms and applications.
|
ESANN | 2016 | 327 |
| Online metric learning for an adaptation to confidence drift.
|
IJCNN | 2016 | 1 |
| Non-negative Kernel Sparse Coding for the Analysis of Motion Data.
|
ICANN | 2016 | 9 |
| KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift.
|
ICDM | 2016 | 233 |
| Certainty-based prototype insertion/deletion for classification with metric adaptation.
|
ESANN | 2015 | 5 |
| Discriminative dimensionality reduction for regression problems using the Fisher metric.
|
IJCNN | 2015 | 4 |
| Combining offline and online classifiers for life-long learning.
|
IJCNN | 2015 | 12 |
| Unsupervised Dimensionality Reduction for Transfer Learning.
|
ESANN | 2015 | 5 |
| Interactive online learning for obstacle classification on a mobile robot.
|
IJCNN | 2015 | 45 |
| Adaptive structure metrics for automated feedback provision in Java programming.
|
ESANN | 2015 | 10 |
| Stationarity of Matrix Relevance LVQ.
|
IJCNN | 2015 | 36 |
| Automatic discovery of metagenomic structure.
|
IJCNN | 2015 | 6 |
| Supervised Generative Models for Learning Dissimilarity Data.
|
ESANN | 2014 | 6 |
| Local Rejection Strategies for Learning Vector Quantization.
|
ICANN | 2014 | 8 |
| Relevance Learning for Dimensionality Reduction.
|
ESANN | 2014 | 2 |
| Adaptive distance measures for sequential data.
|
ESANN | 2014 | 10 |
| Rejection strategies for learning vector quantization.
|
ESANN | 2014 | 13 |
| Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
|
ICANN | 2014 | 3 |
| Learning and modeling big data.
|
ESANN | 2014 | 11 |
| Preface: Intelligent interactive data visualization.
|
DMKD | 2013 | 0 |
| Semi-Supervised Vector Quantization for proximity data.
|
ESANN | 2013 | 8 |
| Sparse approximations for kernel learning vector quantization.
|
ESANN | 2013 | 6 |
| Visualizing dependencies of spectral features using mutual information.
|
ESANN | 2013 | 1 |
| Out-of-sample kernel extensions for nonparametric dimensionality reduction.
|
ESANN | 2012 | 39 |
| Linear basis-function t-SNE for fast nonlinear dimensionality reduction.
|
IJCNN | 2012 | 34 |
| Relevance learning for time series inspection.
|
ESANN | 2012 | 2 |
| Recent developments in clustering algorithms.
|
ESANN | 2012 | 12 |
| Learning Relevant Time Points for Time-Series Data in the Life Sciences.
|
ICANN | 2012 | 8 |
| Relevance learning for short high-dimensional time series in the life sciences.
|
IJCNN | 2012 | 1 |
| Visualizing the quality of dimensionality reduction.
|
ESANN | 2012 | 0 |
| Patch Affinity Propagation.
|
ESANN | 2011 | 3 |
| Generalized functional relevance learning vector quantization.
|
ESANN | 2011 | 12 |
| Accelerating Kernel Neural Gas.
|
ICANN | 2011 | 2 |
| Supervised dimension reduction mappings.
|
ESANN | 2011 | 6 |
| Regularization in matrix relevance learning.
|
IEEE Trans. Neural Networks | 2010 | 102 |
| Sparse representation of data.
|
ESANN | 2010 | 2 |
| Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
|
ESANN | 2010 | 73 |
| Relevance learning in generative topographic maps.
|
ESANN | 2010 | 0 |
| Relational Generative Topographic Map.
|
ESANN | 2010 | 0 |
| Equilibrium properties of off-line LVQ.
|
ESANN | 2009 | 5 |
| Nonlinear Discriminative Data Visualization.
|
ESANN | 2009 | 12 |
| Median Variant of Fuzzy c-Means.
|
ESANN | 2009 | 4 |
| Hyperparameter Learning in Robust Soft LVQ.
|
ESANN | 2009 | 11 |
| Recent advances in efficient learning of recurrent networks.
|
ESANN | 2009 | 31 |
| Parallelizing single patch pass clustering.
|
ESANN | 2008 | 7 |
| Magnification Control in Relational Neural Gas.
|
ESANN | 2008 | 5 |
| Matrix Learning for Topographic Neural Maps.
|
ICANN | 2008 | 10 |
| Intuitive Clustering of Biological Data.
|
IJCNN | 2007 | 5 |
| Relevance matrices in LVQ.
|
ESANN | 2007 | 43 |
| On the dynamics of Vector Quantization and Neural Gas.
|
ESANN | 2007 | 4 |
| How to process uncertainty in machine learning?.
|
ESANN | 2007 | 23 |
| Dynamics and Generalization Ability of LVQ Algorithms.
|
JMLR | 2007 | 118 |
| Neural networks and machine learning in bioinformatics - theory and applications.
|
ESANN | 2006 | 23 |
| Magnification control for batch neural gas.
|
ESANN | 2006 | 0 |
| Margin based Active Learning for LVQ Networks.
|
ESANN | 2006 | 0 |
| The dynamics of Learning Vector Quantization.
|
ESANN | 2005 | 8 |
| Relevance determination in reinforcement learning.
|
ESANN | 2005 | 2 |
| Classification using non-standard metrics.
|
ESANN | 2005 | 24 |
| Relevance learning for mental disease classification.
|
ESANN | 2005 | 4 |
| A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
|
IJCNN | 2004 | 1 |
| Neural methods for non-standard data.
|
ESANN | 2004 | 36 |
| Self-organizing context learning.
|
ESANN | 2004 | 14 |
| Improving iterative repair strategies for scheduling with the SVM.
|
ESANN | 2003 | 22 |
| Unsupervised Recursive Sequence Processing.
|
ESANN | 2003 | 42 |
| Mathematical Aspects of Neural Networks.
|
ESANN | 2003 | 41 |
| Recurrent networks for structured data - A unifying approach and its properties.
|
Cognitive System Research | 2002 | 30 |
| Batch-RLVQ.
|
ESANN | 2002 | 5 |
| Learning Vector Quantization for Multimodal Data.
|
ICANN | 2002 | 18 |
| Rule Extraction from Self-Organizing Networks.
|
ICANN | 2002 | 28 |
| Perspectives on learning with recurrent neural networks.
|
ESANN | 2002 | 18 |
| A general framework for unsupervised processing of structured data.
|
ESANN | 2002 | 0 |
| Architectural Bias in Recurrent Neural Networks - Fractal Analysis.
|
ICANN | 2002 | 0 |
| Generalized Relevance LVQ for Time Series.
|
ICANN | 2001 | 20 |
| On the Generalization Ability of Recurrent Networks.
|
ICANN | 2001 | 5 |
| Relevance determination in Learning Vector Quantization.
|
ESANN | 2001 | 94 |
| Input pruning for neural gas architectures.
|
ESANN | 2001 | 4 |
| Limitations of hybrid systems.
|
ESANN | 2000 | 2 |
| Approximation capabilities of folding networks.
|
ESANN | 1999 | 17 |
| Training a sigmoidal network is difficult.
|
ESANN | 1998 | 0 |
| Generalization of Elman Networks.
|
ICANN | 1997 | 13 |