Search Results for author: Stefan Lüdtke

Found 17 papers, 5 papers with code

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

no code implementations2 Jul 2024 Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Comparative studies assessing the performance of models typically consist of model-centric evaluation setups with overly standardized data preprocessing.

Feature Engineering Hyperparameter Optimization +2

Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods

1 code implementation1 Jul 2024 Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e. g., due to different sites or repeated measurements).

Clustering Multi-class Classification

GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data

3 code implementations29 Sep 2023 Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization.

On the Aggregation of Rules for Knowledge Graph Completion

no code implementations1 Sep 2023 Patrick Betz, Stefan Lüdtke, Christian Meilicke, Heiner Stuckenschmidt

Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models.

Knowledge Graph Completion

Towards Machine Learning-based Fish Stock Assessment

no code implementations7 Aug 2023 Stefan Lüdtke, Maria E. Pierce

The accurate assessment of fish stocks is crucial for sustainable fisheries management.


GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent

1 code implementation5 May 2023 Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability.

Binary Classification

Leveraging Planning Landmarks for Hybrid Online Goal Recognition

no code implementations25 Jan 2023 Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Furthermore, we show that the utilized planning landmark based approach, which was so far only evaluated on artificial benchmark domains, achieves also good recognition performance when applied to a real-world cooking scenario.

Intrusion Detection

Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition

no code implementations13 Jan 2023 Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Heiner Stuckenschmidt

We empirically show that the proposed extension not only outperforms the purely planning-based- and purely data-driven goal recognition methods but is also able to recognize the correct goal more reliably, especially when only a small number of observations were seen.

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

no code implementations18 Jul 2022 Maximilian Popko, Sebastian Bader, Stefan Lüdtke, Thomas Kirste

We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments.

Human Activity Recognition

Explaining Neural Networks without Access to Training Data

1 code implementation10 Jun 2022 Sascha Marton, Stefan Lüdtke, Christian Bartelt, Andrej Tschalzev, Heiner Stuckenschmidt

We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues.

Exchangeability-Aware Sum-Product Networks

1 code implementation11 Oct 2021 Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable.

State-Space Abstractions for Probabilistic Inference: A Systematic Review

no code implementations18 Apr 2018 Stefan Lüdtke, Max Schröder, Frank Krüger, Sebastian Bader, Thomas Kirste

Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches.

Multiple Object Tracking

Lifted Filtering via Exchangeable Decomposition

no code implementations31 Jan 2018 Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste

We present a model for exact recursive Bayesian filtering based on lifted multiset states.

Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems

no code implementations20 Jul 2017 Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas Kirste

We sketch a novel inference algorithm that provides a solution by incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset Rewriting Systems, and Computational State Space Models.

Activity Recognition

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