20 citations · 158 across the 42 of their papers we have counts for
44 papers
MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off
Ibram Abdelmalak, Mischa Putzke, Jungmin Choi +3
Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet the…
Do Tabular Foundation Models Agree with Themselves?
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1
Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate the Bayesian posterior predic…
The Importance of Encoder Choice:A Tabular-Image Study
Ilia Koloiarov, Diego Coello de Portugal Mecke, Vijaya Krishna Yalavarthi +2
Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emph{plain MLP} as the encoder. Yet…
Gaussian Mixture Copula Processes for Irregular Time Series
Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1
We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is expressive and marginalization c…
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-Thieme
Large Language Models (LLMs) have experienced significant growth and development in recent years. However, performing inference on LLMs remains costly, especially for long-context…
LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
Ahmad Bdeir, Johannes Burchert, Tom Hanika +2
Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high in…
HexFormer: Hyperbolic Vision Transformer with Exponential Map Aggregation
Haya Alyoussef, Ahmad Bdeir, Diego Coello de Portugal Mecke +3
Data across modalities such as images, text, and graphs often contains hierarchical and relational structures, which are challenging to model within Euclidean geometry. Hyperbolic…
What is the dimension of your binary data? -- and how to compute it quickly
Tom Hanika, Tobias Hille
Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dim…
A Repository for Formal Contexts
Tom Hanika, Robert Jäschke
Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of…
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Tobias Hille, Maximilian Stubbemann, Tom Hanika
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning re…
The Geometric Structure of Topic Models
Johannes Hirth, Tom Hanika
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics.…
Towards Ordinal Data Science
Gerd Stumme, Dominik Dürrschnabel, Tom Hanika
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amo…
Automatic Textual Explanations of Concept Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such…
Selecting Features by their Resilience to the Curse of Dimensionality
Maximilian Stubbemann, Tobias Hille, Tom Hanika
Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity fea…
Ordinal Motifs in Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of…
Scaling Dimension
Bernhard Ganter, Tom Hanika, Johannes Hirth
Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond. Its mathematical theory, as elaborated in the last chapter of the FCA monograph, still has room…
Conceptual Views on Tree Ensemble Classifiers
Tom Hanika, Johannes Hirth
Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance…
Discovering Locally Maximal Bipartite Subgraphs
Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally…
Intrinsic Dimension for Large-Scale Geometric Learning
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophistic…
Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis
Johannes Hirth, Tom Hanika
We introduce \emph{conceptual views} as a formal framework grounded in Formal Concept Analysis for globally explaining neural networks. Experiments on twenty-four ImageNet models a…
Research Topic Flows in Co-Authorship Networks
Bastian Schäfermeier, Johannes Hirth, Tom Hanika
In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertis…
Mapping Research Trajectories
Bastian Schäfermeier, Gerd Stumme, Tom Hanika
Steadily growing amounts of information, such as annually published scientific papers, have become so large that they elude an extensive manual analysis. Hence, to maintain an over…
Towards Explainable Scientific Venue Recommendations
Bastian Schäfermeier, Gerd Stumme, Tom Hanika
Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the…
Topological Indoor Mapping through WiFi Signals
Bastian Schaefermeier, Gerd Stumme, Tom Hanika
The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In…